analysis and synthesis models

Analysis and synthesis phase of compiler

There are two main phases in the compiler.

1. Analysis - Front end of a compiler

2. Synthesis - Back end of a compiler

In this tutorial, we will learn the role of analysis and synthesis phase of a compiler.

Analysis phase of compiler

Analysis phase reads the source program and splits it into multiple tokens and constructs the intermediate representation of the source program.

And also checks and indicates the syntax and semantic errors of a source program.

It collects information about the source program and prepares the symbol table . Symbol table will be used all over the compilation process.

This is also called as the front end of a compiler.

Synthesis phase of compiler

It will get the analysis phase input(intermediate representation and symbol table) and produces the targeted machine level code.

This is also called as the back end of a compiler.

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Synthesis Phase in Compiler Design

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Pre-requisites: Phases of a Compiler

The synthesis phase, also known as the code generation or code optimization phase, is the final step of a compiler . It takes the intermediate code generated by the front end of the compiler and converts it into machine code or assembly code, which can be executed by a computer. The intermediate code can be in the form of an abstract syntax tree, intermediate representation, or some other form of representation. 

The back end of the compiler, which includes the synthesis phase, is responsible for generating efficient and fast code by performing various optimization techniques such as register allocation, instruction scheduling, and memory management. These optimization techniques are intended to minimize the code size and increase performance by reducing the number of instructions and cycles required for execution.

 The output of the synthesis phase is a binary file that can be loaded into memory and executed by the CPU. The generated code is platform-specific and depends on the target architecture that the compiler was designed for. The synthesis phase is crucial for producing efficient and high-performance code that can run on different platforms.

The synthesis phase, also known as the code generation phase, is the final phase of the compilation process in which the compiler takes the optimized abstract syntax tree (AST) generated in the previous phase and generates machine code or intermediate code that can be executed by the target platform.

There are several potential issues that may arise during the synthesis phase of compilation. Some common issues include:

• Code generation errors: These are errors that occur when the compiler is unable to generate machine code or intermediate code that is correct or complete. This may be due to errors in the AST or problems with the code generator itself.
• Code size and performance: The compiler may generate machine code that is larger or slower than desired, which can impact the performance of the resulting program.
• Compatibility issues: The generated code may not be compatible with the target platform or with other libraries or frameworks that the program is intended to use.
• Linking errors: If the generated code references external symbols or functions that are not defined, the linker may generate errors when trying to combine the generated code with other object files.

To address these and other issues that may arise during the synthesis phase, compiler designers and developers must carefully design and test their code generators to ensure that they produce high-quality machine code or intermediate code.

Some key important of the synthesis phase include:

• Generating machine code: The synthesis phase takes the intermediate code generated in the previous phase and generates machine code or executable code that can be run on a specific computer architecture.
• Improving performance: The synthesis phase performs various optimization techniques such as instruction selection, register allocation, and memory management to improve the performance of the generated code.
• Creating executable files: The final output of the synthesis phase is typically a file containing machine code or assembly code that can be directly executed by the computer’s CPU. This allows the code to be run on the target platform.
• Language independent: As the synthesis phase is typically platform dependent, it can be applied to a wide variety of programming languages and platforms, making it a versatile and reusable component in a compiler.
• Enabling optimization: The synthesis phase uses the information gathered by the analysis phase to perform various optimization techniques to improve the performance of the generated code.
• Improving the development process: By generating high-performance machine code, the synthesis phase can help reduce the need for debugging and testing later on and make the development process more efficient.

Applications

• Generating machine code or executable code for a specific platform: The synthesis phase takes the intermediate code and generates code that can be run on a specific computer architecture.
• Instruction selection: The compiler selects appropriate machine instructions for the target platform to implement the intermediate code.
• Register allocation: The compiler assigns values to registers to improve the performance of the generated code.
• Memory management: The compiler manages the allocation and deallocation of memory to ensure the generated code runs efficiently.
• Optimization: The compiler performs various optimization techniques such as dead code elimination, constant folding, and common subexpression elimination to improve the performance of the generated code.
• Creating executable files: The final output of the synthesis phase is typically a file containing machine code or assembly code that can be directly executed by the computer’s CPU.

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• Mar 1, 2008

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*Written for Interactions magazine by Hugh Dubberly, Shelley Evenson, and Rick Robinson.*

The simplest way to describe the design process is to divide it into two phases: analysis and synthesis. Or preparation and inspiration. But those descriptions miss a crucial element—the connection between the two, the active move from one state to another, the transition or transformation that is at the heart of designing. How do designers move from analysis to synthesis? From problem to solution? From current situation to preferred future? From research to concept? From constituent needs to proposed response? From context to form?

How do designers bridge the gap?

The bridge model illustrates one way of thinking about the path from analysis to synthesis—the way in which the use of models to frame research results acts as a basis for framing possible futures. It says something more than “then the other thing happens.” It shows how designers and researchers move up through a level of analysis in order to move forward through time to the next desired state. And models act as the vehicle for that move.

The bridge model here is organized as a two-by-two matrix. The left column represents analysis (the problem, current situation, research, constituent needs, context). The right column represents synthesis (the solution, preferred future, concept, proposed response, form). The bottom row represents the concrete world we inhabit or could inhabit. The top row represents abstractions, models of what is or what could be, which we imagine and share with others.

**Analysis-Synthesis Bridge Model**

Ideally, the design process begins in the lower-left quadrant with observation and investigation—an inventory (or description) of the current situation. As the process moves forward, it moves to the upper-left quadrant. We make sense of research by analysis, filtering data we collect to highlight points we decide are important or using tools we’re comfortable with to sort, prioritize, and order. We frame the current situation, but move out of the strictly concrete. We define the problem. We interpret. Analysis begins as thoughtful reflection on the present and continues as conversation with the possible. Crucial for progress is documenting and visualizing our analysis, making it possible for us to come back to it, making it possible to imagine alternatives, making it possible ultimately to discuss and agree with others on our framing and definition. We might write down a list of findings or a statement defining the problem. Better still is writing a story. A story describes actors and actions; it suggests relationships, which we may represent in visual form. A story of what happens suggests a model of what is—an interpretation of our research. The process of coming to a shared representation externalizes individual thinking and helps build trust across disciplines and stakeholders.

Having agreed on a model of what is (framed the current situation, defined the problem) then the other side of the coin (the preferred future, the solution) is implied. An interpretation provides “a description of the everyday in such a way as to see how it might be different, better, or new [1].” We can devise stories about what could happen. We can model alternatives in relation to our first model. In doing so, we’ve moved to the upper-right quadrant, to the use and development of models of what could be. It is in the realm of abstraction—by thinking with models—that we bridge the gap between analysis and synthesis. These models are hypotheses, speculations, imagined alternatives to the concrete we started with, but they are still abstract themselves. It is easy to “play” with models at this point, to test and explore. But design requires that the work return to the concrete, that we make things real, realize our models as prototypes or even finished form. This is the lower-right quadrant. Of course, results improve with iteration. Submitting the new prototype to testing, further observation and investigation, continuing around the quadrants, we learn and refine our work. The bridge model has several antecedents and variations.

**Robinson Model**

The bridge model grew out of personal discussions over the past few years. Rick Robinson has written about “the space in between” research and concept. He has described anthropologist Clifford Geertz’s essay, “Deep Play: Notes on the Balinese Cockfight,” as an example of abstracting a model from research, and one that parallels strongly the moves that other forms of research and design make in moving from description through interpretation to application. “[The construct of] Deep Play becomes a lens through which Geertz can show what’s important about the Balinese cockfight, and his colleagues can understand important underlying factors in something like fan riots at soccer matches [1].”

**Beer Model**

Writing about the relationship of science to management, Stafford Beer presented a more elaborate model of the move from cases to consensus, from particular to general. He points out that several levels of models are involved [2].

**Alexander Model**

At the beginning of his career, Christopher Alexander described a six-part model. It differs from the bridge model in two important respects. First, Alexander explicitly separates the mental picture (model) from a formal picture of the mental picture (a representation of the model). Second, his notion of a model (at that time at least) was highly mathematical [3].

**Kumar Model**

Vijay Kumar has proposed a model of the innovation process.[4] He frames it as a two-by- two matrix, moving from research, to “Framing Insights,” “Exploring Concepts,” and “Making Plans.” He notes, “’Framing Insights’ are primarily about descriptive modeling, creating abstract mental pictures about the patterns that we recognize about reality. ‘Exploring Concepts’ and ‘Making Plans’ are about prescriptive modeling.” Where the bridge model forefronts the role of models, Kumar’s model forefronts steps that make use of modeling. He recently published a wonderful poster that maps the steps in the “innovation process” to a series of methods.

**Kaiser-IDEO Model**

During the process of writing this article, interactions co-editor Richard Anderson pointed out this model of the innovation process. Christi Zuber reports that Kaiser Permanente’s Innovation Center (working with IDEO) developed this model in 2004 as part of an innovation toolkit created for use inside Kaiser. This model is similar to Kumar’s model, but the Kaiser model emphasizes storytelling and brainstorming as key methods.

**Suri-IDEO Model**

Responding to questions about the origin of the Kaiser/IDEO model, Jane Fulton Suri supplied this recent model of the process of moving from synthesis to strategy. It shares the same basic structure as the Robinson model; though synthesis (depicted as the right column in other models) is here depicted as the left column. The framing of models as a link between patterns and principles is a useful addition [5].

While practitioners and educators increasingly make use of models, few forefront the role of modeling in public summaries of their work processes. Glossing over modeling can limit design to the world of form-making and misses an opportunity to push toward interaction and experience. We see modeling becoming an integral part of practice, especially in designing software, services, and other complex systems.

The bridge model makes explicit the role of modeling in the design process. Explicit modeling is useful in at least two ways. First, it accelerates the design process by encouraging team members to understand and agree on the elements of a system and how those elements interact with each other and their environment. Second, by making the elements and their interactions visible, it reduces the likelihood of overlooking differences in point of view, which might otherwise eventually derail a project.

Explicit modeling also helps scale the design process. It enables designers to develop larger and more complex systems and makes the process of working with larger and more complex organizations easier. Discussing the role of modeling in design also invites comparison and interaction with other disciplines that use models. Ideally, practitioners that use models may, over time, be able to see patterns across their models that will advance the practice of design.

Joanne Mendel and Jan Yeager build on the bridge model in their article [Knowledge Visualization in Design Practice][1].

[1]: http://piim.newschool.edu/journal/issues/2010/03/pdfs/ParsonsJournalForInformationMapping_Mendel-Joanne+Yeager-Jan.pdf “Knowledge Visualization in Design Practice”

Yggdrasil 2008 - Day 2 | Searchnuggets

Oct 19, 2008 5:53 am

[…] from Objectware gave us a seemingly complete walk through his UX toolbox. Referring to the analysis-synthesis bridge model, he described a large set of methods for user observation, interviews, workshop techniques, user […]

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Putting people first » Dubberly Design articles

Feb 13, 2009 3:09 am

[…] The analysis-synthesis bridge model Written for Interactions magazine by Hugh Dubberly, Shelley Evenson, and Rick Robinson – 1 March 2008 The simplest way to describe the design process is to divide it into two phases: analysis and synthesis. Or preparation and inspiration. But those descriptions miss a crucial element—the connection between the two, the active move from one state to another, the transition or transformation that is at the heart of designing. How do designers move from analysis to synthesis? From problem to solution? From current situation to preferred future? From research to concept? From constituent needs to proposed response? From context to form? […]

Jan 4, 2015 1:01 pm

Consider the primitive origins of analysis and synthesis in ancient or pre-historic times.

For example, a warrior finds a spear lost by another tribe. How will he make one like it? He dismantles in into separate pieces. (Analysis!) He spreads them out in an orderly pattern on a flat sheet or surface and, of course, exercises his imagination. (Sounds like “spreadsheeting” to me.) Then he puts the spear (or one like it) back together again. (Synthesis!)

The use of models (objects, pictures, symbols) representing other things seems to me to be the next step.

Thank you for an interesting scholarly article in this subject area. By the way, if there was an ancient word for “spreadsheeting”, I would be happy to learn of it.

Dec 28, 2021 10:41 pm

Could this not be deemed similar to the TRIZ analysis / synthesis model proposed by Altshuller?

https://upload.wikimedia.org/wikipedia/commons/a/a2/Prism_of_TRIZ_Oxford_Creativity.png

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Gega L, Jankovic D, Saramago P, et al. Digital interventions in mental health: evidence syntheses and economic modelling. Southampton (UK): NIHR Journals Library; 2022 Jan. (Health Technology Assessment, No. 26.1.)

Digital interventions in mental health: evidence syntheses and economic modelling.

Appendix 1 methods for statistical analysis and synthesis model.

Reproduced with permission from Saramago et al. 124 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: https://creativecommons.org/licenses/by/4.0/ . The text below includes minor additions and formatting changes to the original text.

• Statistical synthesis model

Using the RE approach, the NMA ANCOVA model used takes the following form:

The set of treatments included in these trials are labelled [A, B, C, . . .], where A is the reference treatment and y 1 i,k and σ 1 i , k 2 are the study i - and arm k -specific post-treatment measurement (the assessment ranging from 3 to 12 weeks) and their associated standard errors. θ i,k is the linear predictor that uses the identity link function, with µ i being the study-specific baseline parameters for the reference treatment b in each study (which is not necessarily the reference treatment of the network, i.e. treatment A) and δ i,b,k is the study-specific relative treatment effects between the treatment included in arm k and the treatment included in the baseline arm b of study i . β bk represent the treatment-specific coefficients that adjust for the pre-treatment (i.e. baseline) measurements y 0 i,k under the ANCOVA model. δ i,k,l are assumed to follow a RE approach with mean d bk and a between-study heterogeneity τ 2 that is assumed to be common across all treatment comparisons to assist identification. For trials that use an active control treatment (i.e. b  ≠  A ) the consistency assumption is imposed in the form of a set of functional relationships among basic parameters (e.g. d Ak ). Note that β AA is assumed to be zero, indicating that patients who did not receive any treatment are expected to neither improve nor worsen during the duration of treatment (i.e. null placebo effect). Finally, we assume that the effect of the baseline measurement is common across all treatments so that β Ak  =  β , implying that, when two active treatments are compared in a trial, the baseline effects are offset. Vague prior distributions were assigned to all parameters [i.e. d Ak , β  ∼  N (0,10 -6 ) and τ  ∼  Unif (0,10)].

Meta-regression is the most commonly employed method to explore the influence of particular study-level covariates on the relative effect. A range of approaches can be used to model comparison-specific treatment–effect interactions. 218 In this analysis, we assumed a common effect interaction (i.e. a single interaction term was assumed to apply to all comparisons with NI), as this was deemed more clinically plausible and also less data demanding. However, this method requires that all studies report data on the covariate(s) in question. For the trials informing the NMA, complete data were obtained for disease severity (as a binary covariate mild to moderate/moderate to severe) but not for the other two potential effect modifiers. Under these circumstances, one option is to exclude studies for which data on the covariate are missing and perform a meta-regression on the subset of studies that provide covariate information; however, this approach may lead to a smaller (with fewer interventions being compared) and ‘weaker’ network (with less evidence informing it). Alternatively, to preserve all studies (and treatments), we may assume that the covariate is distributed across studies in accordance with a beta distribution, the hyperparameters of which are assigned non-informative priors and are estimated in the model through the MCMC simulation to impute missing covariate information (multiple imputation procedure assuming ‘missingness’ mechanism of ‘missing at random’). The meta-regression model extends the aforementioned NMA ANCOVA model so that the linear predictor is now:

B Ak are again assumed independent of treatment comparison so that B Ak  =  B , which represents the additional effect that is observed not because of the treatment, but because of the interaction of the treatment with the study-level covariate. X i represent the study-level covariate values, and are assigned a beta distribution with hyperparameters a , b , which are estimated in the model and are assigned vague priors a , b  ∼  Unif (0,1000). As X i are proportions, a beta distribution is perhaps the most reasonable distributional assumption. The effect modification for the reference treatment is also assumed to be zero.

• WinBUGS code for main synthesis model

The WinBUGS modelling code is provided followed by a summary table of all variables included in the data set and R code describing the specification of initial values for two chains.

WinBUGS model code

for(i in 1:NS) {

 w[i,1]<- 0

 delta[i,1]<- 0

 mu[i] ∼ dnorm(0,1.0E-6)

 for (k in 1:na[i]) {

  y1[i,k] ∼ dnorm(theta[i,k], prec[i,k]) #likelihood function

  theta[i,k] <- mu[i] + delta[i,k]

  var[i,k] <- pow(se1[i,k], 2)

  prec[i,k] <- 1/var[i,k]

  dev[i,k] <- (y1[i,k] - theta[i,k]) * (y1[i,k] – theta[i,k]) * prec[i,k] #residual deviance

  }

resdev[i] <- sum(dev[i,1:na[i]])

 for (k in 2:na[i]) {

#consistency model for treatment effects and baseline adjustment

  delta[i,k] ∼ dnorm(md[i,k],precd[i,k])

  md[i,k]<- d[t[i,k]] - d[t[i,1]] + (b_base[t[i,k]] – b_base[t[i,1]]) * y0[i,k] + sw[i,k]

  precd[i,k] <- pre * 2 * (k – 1)/k

#correction for multi-arm trials

  w[i,k]<- delta[i,k] – d[t[i,k]] + d[t[i,1]]

  sw[i,k]<- sum(w[i,1:k-1])/(k – 1)

#total Residual Deviance

totresdev <- sum(resdev[])

d[1]<-0

for (k in 2:NT) {

#prior on treatment effects and baseline score effects

 d[k] ∼ dnorm(0,1.0E-6)

 b_base[k] <- b_basey

#prior on random treatment effect variance

tau ∼ dunif(0,10)

tau.sq<- tau * tau

pre<- 1/(tau.sq)

#prior on impact of baseline score on final outcome score

b_basey ∼ dnorm(0,1.0E-6)

b_base[1]<-0

# pairwise effects

for (c in 1:(NT – 1)) {

 for (k in (c + 1):NT) {

  ef[c,k] <- d[k] – d[c]

# Treatment A baseline, based on average of the trials including No intervention

for (i in 1:NS) {

 mu1[i] <- mu[i] * equals(t[i,1],1)

mn.mu1<- sum(mu1[]) / 6

#Posterior distributions of absolute post-treatment scores

for (k in 1:NT) {

 T[k]<- mn.mu1 + d[k] + b_base[k]*mn.mu1

# ranking and prob{treatment k is the best}

 rk[k]<- NT + 1 – rank(T[],k)

 best[k]<- equals(rk[k],7)

Description of data sets and variables

The R code used to generate initial values (only one set shown for didactic purposes) was:

list(d = c(NA,0,0,0,0,0,0), mu = c(0,0,0,0,0, 0,0,0,0,0,0,0,0), b_basey = c(0), tau = c(1))

• Cite this Page Gega L, Jankovic D, Saramago P, et al. Digital interventions in mental health: evidence syntheses and economic modelling. Southampton (UK): NIHR Journals Library; 2022 Jan. (Health Technology Assessment, No. 26.1.) Appendix 1, Methods for statistical analysis and synthesis model.
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Synthesising the data

Synthesis is a stage in the systematic review process where extracted data, that is the findings of individual studies, are combined and evaluated.   

The general purpose of extracting and synthesising data is to show the outcomes and effects of various studies, and to identify issues with methodology and quality. This means that your synthesis might reveal several elements, including:  

• overall level of evidence  
• the degree of consistency in the findings  
• what the positive effects of a drug or treatment are ,  and what these effects  are  based on  
• how many studies found a relationship or association between two components, e.g. the impact of disability-assistance animals on the psychological health of workplaces

There are two commonly accepted methods of synthesis in systematic reviews:  

Qualitative data synthesis

• Quantitative data synthesis  (i.e. meta-analysis)  

The way the data is extracted from your studies, then synthesised and presented, depends on the type of data being handled.  

In a qualitative systematic review, data can be presented in a number of different ways. A typical procedure in the health sciences is  thematic analysis .

Thematic synthesis has three stages:

• the coding of text ‘line-by-line’
• the development of ‘descriptive themes’
• and the generation of ‘analytical themes’

If you have qualitative information, some of the more common tools used to summarise data include:  

• textual descriptions, i.e. written words  
• thematic or content analysis

Example qualitative systematic review

A good example of how to conduct a thematic analysis in a systematic review is the following journal article on cancer patients. In it, the authors go through the process of:

• identifying and coding information about the selected studies’ methodologies and findings on patient care
• organising these codes into subheadings and descriptive categories
• developing these categories into analytical themes

What Facilitates “Patient Empowerment” in Cancer Patients During Follow-Up: A Qualitative Systematic Review of the Literature

Quantitative data synthesis

In a quantitative systematic review, data is presented statistically. Typically, this is referred to as a  meta-analysis .

The usual method is to combine and evaluate data from multiple studies. This is normally done in order to draw conclusions about outcomes, effects, shortcomings of studies and/or applicability of findings.

Remember, the data you synthesise should relate to your research question and protocol (plan). In the case of quantitative analysis, the data extracted and synthesised will relate to whatever method was used to generate the research question (e.g. PICO method), and whatever quality appraisals were undertaken in the analysis stage.

If you have quantitative information, some of the more common tools used to summarise data include:  

• grouping of similar data, i.e. presenting the results in tables  
• charts, e.g. pie-charts  
• graphical displays, i.e. forest plots

Example of a quantitative systematic review

A quantitative systematic review is a combination of qualitative and quantitative, usually referred to as a meta-analysis.

Effectiveness of Acupuncturing at the Sphenopalatine Ganglion Acupoint Alone for Treatment of Allergic Rhinitis: A Systematic Review and Meta-Analysis

About meta-analyses

A systematic review may sometimes include a  meta-analysis , although it is not a requirement of a systematic review. Whereas, a meta-analysis also includes a systematic review.  

A meta-analysis is a statistical  analysis  that combines data from  previous  studies  to calculate an overall result.

One way of accurately representing all the data is in the form of a  forest plot . A forest plot is a way of combining the results of multiple studies in order to show point estimates arising from different studies of the same condition or treatment.

It is comprised of a graphical representation and often also a table. The graphical display shows the mean value for each study and often with a confidence interval (the horizontal bars). Each mean is plotted relative to the vertical line of no difference.

The following is an example of the graphical representation of a forest plot.

“File:The effect of zinc acetate lozenges on the duration of the common cold.svg”  by  Harri Hemilä  is licensed under  CC BY 3.0

Watch the following short video where a social health example is used to explain how to construct a forest plot graphic.

Forest Plots: Understanding a Meta-Analysis in 5 Minutes or Less (5:38 mins)

Forest Plots – Understanding a Meta-Analysis in 5 Minutes or Less  (5:38 min) by The NCCMT ( YouTube )

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Chapter 6: Analysis and Synthesis

What does it mean to know something? How would you explain the process of thinking? In the 1950s, educational theorist Benjamin Bloom proposed that human cognition, thinking and knowing, could be classified by six categories. [1] Hierarchically arranged in order of complexity, these steps were:

• application
• comprehension

Since his original model, the taxonomy has been revised, as illustrated in the list below:

• understanding
• remembering

A more complex version of Bloom’s taxonomy is displayed like a flower.

Look closely at the lists above.

• Each word is an action verb instead of a noun (e.g., “applying” instead of “application”);
• Some words have been changed for different synonyms;
• One version holds “creating” above “evaluating”;
• And, most importantly, other versions are reshaped into a circle, as pictured above. [2]

What do you think the significance of these changes is?

I introduce this model of cognition to contextualize analysis as a cognitive tool which can work in tandem with other cognitive tasks and behaviors. Analysis is most commonly used alongside synthesis . To proceed with the LEGO® example from Chapter 4, consider my taking apart the castle as an act of analysis. I study each face of each block intently, even those parts that I can’t see when the castle is fully constructed. In the process of synthesis, I bring together certain blocks from the castle to instead build something else—let’s say, a racecar. By unpacking and interpreting each part , I’m able to build a new whole . [3]

In a text wrestling essay, you’re engaging in a process very similar to my castle-to-racecar adventure. You’ll encounter a text and unpack it attentively, looking closely at each piece of language, its arrangement, its signification, and then use it to build an insightful, critical insight about the original text. I might not use every original block, but by exploring the relationship of part-to-whole, I better understand how the castle is a castle. In turn, I am better positioned to act as a sort of tour guide for the castle or

a mechanic for the racecar, able to show my readers what about the castle or racecar is important and to explain how it works.

In this chapter, you’ll learn about crafting a thesis for a text wrestling essay and using evidence to support that thesis . As you will discover, an analytical essay involves every tier of Bloom’s Taxonomy, arguably even including “judgement” because your thesis will present an interpretation that is evidence-based and arguable.

So What? Turning Observations into a Thesis

It’s likely that you’ve heard the term “thesis statement” multiple times in your writing career. Even though you may have some idea what a thesis entails already, it is worth reviewing and unpacking the expectations surrounding a thesis, specifically in a text wrestling essay.

A thesis statement is a central, unifying insight that drives your analysis or argument. In a typical college essay, this insight should be articulated in one to three sentences, placed within the introductory paragraph or section. As we’ll see below, this is not always the case, but it is what many of your audiences will expect. To put it simply, a thesis is the “So what?” of an analytical or persuasive essay. It answers your audience when they ask, Why does your writing matter? What bigger insights does it yield about the subject of analysis? About our world?

Thesis statements in most rhetorical situations advocate for a certain vision of a text, phenomenon, reality, or policy. Good thesis statements support such a vision using evidence and thinking that confirms, clarifies, demonstrates, nuances, or otherwise relates to that vision. In other words, a thesis is “a proposition that you can prove with evidence…, yet it’s one you have to prove, that isn’t obviously true or merely factual.” [4]

In a text wrestling analysis, a thesis pushes beyond basic summary and observation. In other words, it’s the difference between:

If you think of your essay as the human body, the thesis is the spine. Yes, the body can still exist without a spine, but its functionings will be severely limited. Furthermore, everything comes back to and radiates out from the spine: trace back from your fingertips to your backbone and consider how they relate. In turn, each paragraph should tie back to your thesis, offering support and clear connections so your reader can see the entire “body” of your essay. In this way, a thesis statement serves two purposes: it is not only about the ideas of your paper, but also the structure .

In addition to capturing the central, unifying insight of your essay, your thesis also acts as a “road map.” It anticipates both content and structure.

The Purdue Online Writing Lab (OWL) [5] suggests this specific process for developing your thesis statement:

Once you’ve read the story or novel closely, look back over your notes for patterns of questions or ideas that interest you. Have most of your questions been about the characters, how they develop or change?

For example: If you are reading Conrad’s The Secret Agent, do you seem to be most interested in what the author has to say about society? Choose a pattern of ideas and express it in the form of a question and an answer such as the following:

Question : What does Conrad seem to be suggesting about early twentieth-century London society in his novel The Secret Agent? Answer:  Conrad suggests that all classes of society are corrupt.

Pitfalls: Choosing too many ideas. Choosing an idea without any support.

Once you have some general points to focus on, write your possible ideas and answer the questions that they suggest.

For example: Question:  How does Conrad develop the idea that all classes of society are corrupt? Answer:  He uses images of beasts and cannibalism whether he’s describing socialites, policemen or secret agents.

To write your thesis statement, all you have to do is turn the question and answer around. You’ve already given the answer, now just put it in a sentence (or a couple of sentences) so that the thesis of your paper is clear.

For example: In his novel, The Secret Agent, Conrad uses beast and cannibal imagery to describe the characters and their relationships to each other. This pattern of images suggests that Conrad saw corruption in every level of early twentieth-century London society.

Now that you’re familiar with the story or novel and have developed a thesis statement, you’re ready to choose the evidence you’ll use to support your thesis. There are a lot of good ways to do this, but all of them depend on a strong thesis for their direction.

For example: Here’s a student’s thesis about Joseph Conrad’s The Secret Agent.

In his novel, The Secret Agent, Conrad uses beast and cannibal imagery to describe the characters and their relationships to each other. This pattern of images suggests that Conrad saw corruption in every level of early twentieth-century London society.

This thesis focuses on the idea of social corruption and the device of imagery. To support this thesis, you would need to find images of beasts and cannibalism within the text.

There are many ways to write a thesis, and your construction of a thesis statement will become more intuitive and nuanced as you become a more confident and competent writer. However, there are a few tried-and-true strategies that I’ll share with you over the next few pages.

The T3 Strategy

Your thesis statement can and should evolve as you continue writing your paper. Often, I prefer to think of a thesis instead as a (hypo)thesis—an informed estimation of how you think your analysis will come together.

T3 is a formula to create a thesis statement. The T (for Thesis) should be the point you’re trying to make—the “So what?” In a text wrestling analysis, you are expected to advocate for a certain interpretation of a text: this is your “So what?” Examples might include:

In “A Wind from the North,” Bill Capossere conveys the loneliness of isolated life

Kate Chopin’s “The Story of an Hour” suggests that marriage can be oppressive to women

But wait—there’s more! In a text wrestling analysis, your interpretation must be based on evidence from that text. Therefore, your thesis should identify both a focused statement of the interpretation (the whole) and also the particular subjects of your observation (the parts of the text you will focus on support that interpretation). A complete T3 thesis statement for a text wrestling analysis might look more like this:

In “A Wind from the North,” Bill Capossere conveys the loneliness of an isolated lifestyle using the motif of snow, the repeated phrase “five or six days” (104), and the symbol of his uncle’s car.

“The Story of an Hour” suggests that marriage can be oppressive to women. To demonstrate this theme, Kate Chopin integrates irony, foreshadowing, and symbols of freedom in the story.

Notice the way the T3 allows for the part-to-whole thinking that underlies analysis:

Whole (T):  Bill Capossere conveys the loneliness of an isolated lifestyle.

• the motif of snow
• the repeated phrase “five or six days” (104)
• the symbol of his uncle’s car.

Whole (T): Bill Capossere conveys the loneliness of an isolated lifestyle.

• foreshadowing
• symbols of freedom

This is also a useful strategy because it can provide structure for your paper: each justifying support for your thesis should be one section of your paper.

• Introduction
• Thesis: In “A Wind from the North,” Bill Capossere conveys the loneliness of an isolated lifestyle using the motif of snow, the repeated phrase “five or six days” (104), and the symbol of his uncle’s car.
• Section on ‘the motif of snow.’ Topic sentence: The recurring imagery of snow creates a tone of frostiness and demonstrates the passage of time.
• Section on ‘the repeated phrase “five or six days” (104).’ Topic sentence: When Capossere repeats “five or six days” (104), he reveals the ambiguity of death in a life not lived.
• Section on ‘the symbol of his uncle’s car.’ Topic sentence: Finally, Capossere’s uncle’s car is symbolic of his lifestyle.

Once you’ve developed a T3 statement, you can revise it to make it feel less formulaic. For example:

In “A Wind from the North,” Bill Capossere conveys the loneliness of an isolated lifestyle by symbolizing his uncle with a “untouchable” car. Additionally, he repeats images and phrases in the essay to reinforce his uncle’s isolation.

“The Story of an Hour,” a short story by Kate Chopin, uses a plot twist to imply that marriage can be oppressive to women. The symbols of freedom in the story create a feeling of joy, but the attentive reader will recognize the imminent irony.

The O/P Strategy

An occasion/position thesis statement is rhetorically convincing because it explains the relevance of your argument and concisely articulates that argument. Although you should already have your position in mind, your rhetorical occasion will lead this statement off: what sociohistorical conditions make your writing timely, relevant, applicable? Continuing with the previous examples:

As our society moves from individualism to isolationism, Bill Capossere’s “A Wind from the North” is a salient example of a life lived alone.

Although Chopin’s story was written over 100 years ago, it still provides insight to gender dynamics in American marriages.

Following your occasion, state your position—again, this is your “So What?” It is wise to include at least some preview of the parts you will be examining.

As our society moves from individualism to isolationism, Bill Capossere’s “A Wind from the North” is a salient example of a life lived alone. Using recurring images and phrases, Capossere conveys the loneliness of his uncle leading up to his death.

Although Chopin’s story was written over 100 years ago, it still provides insight to gender dynamics in American marriages. “The Story of an Hour” reminds us that marriage has historically meant a surrender of freedom for women.

Research Question and Embedded Thesis

There’s one more common style of thesis construction that’s worth noting, and that’s the inquiry-based thesis. (Read more about inquiry-based research writing in Chapter Eight). For this thesis, you’ll develop an incisive and focused question which you’ll explore throughout the course of the essay. By the end of the essay, you will be able to offer an answer (perhaps a complicated or incomplete answer, but still some kind of answer) to the question. This form is also referred to as the “embedded thesis” or “delayed thesis” organization.

Although this model of thesis can be effectively applied in a text wrestling essay, it is often more effective when combined with one of the other methods above.

Consider the following examples:

Bill Capossere’s essay “A Wind from the North” suggests that isolation results in sorrow and loneliness; is this always the case? How does Capossere create such a vision of his uncle’s life?

Many people would believe that Kate Chopin’s story reflects an outdated perception of marriage—but can “The Story of an Hour” reveal power imbalances in modern relationships, too?

You may note that these three thesis strategies can be combined to create nuanced and attention-grabbing thesis statements.

Synthesis: Using Evidence to Explore Your Thesis

Now that you’ve considered what your analytical insight might be (articulated in the form of a thesis), it’s time to bring evidence in to support your analysis—this is the synthesis part of Bloom’s Taxonomy earlier in this chapter. Synthesis refers to the creation of a new whole (an interpretation) using smaller parts (evidence from the text you’ve analyzed).

There are essentially two ways to go about collecting and culling relevant support from the text with which you’re wrestling. In my experience, students are split about evenly on which option is better for them:

Option #1: Before writing your thesis , while you’re reading and rereading your text, annotate the page and take notes. Copy down quotes, images, formal features, and themes that are striking, exciting, or relatable. Then, try to group your collection of evidence according to common traits. Once you’ve done so, choose one or two groups on which to base your thesis.

Option #2: After writing your thesis , revisit the text looking for quotes, images, and themes that support, elaborate, or explain your interpretation. Record these quotes, and then return to the drafting process.

Once you’ve gathered evidence from your focus text, you should weave quotes, paraphrases, and summaries into your own writing. A common misconception is that you should write “around” your evidence, i.e. choosing the direct quote you want to use and building a paragraph around it. Instead, you should foreground your

interpretation and analysis, using evidence in the background to explore and support that interpretation. Lead with your idea, then demonstrate it with evidence; then, explain how your evidence demonstrates your idea.

The appropriate ratio of evidence (their writing) to exposition (your writing) will vary depending on your rhetorical situation, but I advise my students to spend at least as many words unpacking a quote as that quote contains. (I’m referring here to Step #4 in the table below.) For example, if you use a direct quote of 25 words, you ought to spend at least 25 words explaining how that quote supports or nuances your interpretation.

There are infinite ways to bring evidence into your discussion, [6] but for now, let’s take a look at a formula that many students find productive as they find their footing in analytical writing.

• Front-load  (1-2 sentences). Set your reader up for the quote using a signpost (also known as a signal phrase; see Chapter Nine). Don’t drop quotes in abruptly: by front-loading, you can guide your reader’s interpretation. Then …
• Q uote, p araphrase, or s ummarize. Use whichever technique is relevant to your rhetorical purpose at that exact point. Then …
• (Cite). Use an in-text citation appropriate to your discipline. It doesn’t matter if you quote, paraphrase, or summarize—all three require a citation. Then …
• Explain, elaborate, analyze (2-3 sentences) . Perhaps most importantly, you need to make the value of this evidence clear to the reader. What does it mean? How does it further your thesis?

What might this look like in practice?

The recurring imagery of snow creates a tone of frostiness and demonstrates the passage of time. Snow brings to mind connotations of wintery cold, quiet, and death as a “sky of utter clarity and simplicity” lingers over his uncle’s home and “it [begins] once more to snow” (Capossere 104). Throughout his essay, Capossere returns frequently to weather imagery, but snow especially, to play on associations the reader has. In this line, snow sets the tone by wrapping itself in with “clarity,” a state of mind. Even though the narrator still seems ambivalent about his uncle, this clarity suggests that he is reflecting with a new and somber understanding.

Snow brings to mind connotations of wintery cold, quiet, and death

as a “sky of utter clarity and simplicity” lingers over his uncle’s home and “it [begins] once more to snow”

(Capossere 104).

Explain/elaborate/analyze

Throughout his essay, Capossere returns frequently to weather imagery, but snow especially, to play on associations the reader has. In this line, snow sets the tone by wrapping itself in with “clarity,” a state of mind. Even though the narrator still seems ambivalent about his uncle, this clarity suggests that he is reflecting with a new and somber understanding.

This might feel formulaic and forced at first, but following these steps will ensure that you give each piece of evidence thorough attention. Some teachers call this method a “quote sandwich” because you put your evidence between two slices of your own language and interpretation.

For more on front-loading (readerly signposts or signal phrases), see the subsection titled “Readerly Signposts” in Chapter Nine.

Idea Generation: Close Reading Graphic Organizer

The first time you read a text, you most likely will not magically stumble upon a unique, inspiring insight to pursue as a thesis. As discussed earlier in this section, close reading is an iterative process, which means that you must repeatedly encounter a text (reread, re-watch, re-listen, etc.) trying to challenge it, interrogate it, and gradually develop a working thesis.

Very often, the best way to practice analysis is collaboratively, through discussion. Because other people will necessarily provide different perspectives through their unique interpretive positions, reading groups can help you grow your analysis. By discussing a text, you open yourself up to more nuanced and unanticipated interpretations influenced by your peers. Your teacher might ask you to work in small groups to complete the following graphic organizer in response to a certain text. (You can also complete this exercise independently, but it might not yield the same results.)

Title and Author of Text: Group Members’ Names: Start by “wading” back through the text. Remind yourself of the general idea and annotate important words, phrases, and passages. As a group, discuss and explain: What could the meaning or message of this text be? What ideas does the text communicate? (Keep in mind, there are an infinite number of “right” answers here.) What patterns do you see in the text (e.g., repetition of words, phrases, sentences, or images; ways that the text is structured)? What breaks in the patterns do you see? What is the effect of these patterns and breaks of pattern? What symbols and motifs do you see in the text? What might they represent? What is the effect of these symbols? What themes do they cultivate or gesture to? What references do you see in the text? Does the author allude to, quote, imitate, or parody another text, film, song, etc.? Does the author play on connotations? What is the effect of these references? What about this text surprises you? What do you get hung up on? Consider Jane Gallop’s brief list from “The Ethics of Reading: Close Encounters” – (1) unusual vocabulary, words that surprise either because they are unfamiliar or because they seem to belong to a different context; (2) words that seem unnecessarily repeated, as if the word keeps insisting on being written; (3) images or metaphors, especially ones that are used repeatedly and are somewhat surprising given the context; (4) what is in italics or parentheses; and (5) footnotes that seem too long [7] – but also anything else that strikes you as a reader. Analytical lenses: Do you see any of the following threads represented in the work? What evidence of these ideas do you see? How do these parts contribute to a whole? Race, Ethnicity, and Nationality Gender and Sexuality Disability Social Class and Economy Ecologies and the Environment (Post)colonialism

Thesis Builder

Your approach to building a thesis will depend on your rhetorical mode; for instance, an analytical thesis (like this one), might not be most appropriate for a persuasive, expository, or research essay.

Your thesis statement can and should evolve as you continue writing your paper: teachers will often refer to a thesis as a “working thesis” because the revision process should include tweaking, pivoting, focusing, expanding, and/or rewording your thesis. The exercise on the next two pages, though, should help you develop a working thesis to begin your project. Following the examples, identify the components of your analysis that might contribute to a thesis statement.

• Topic . Name your focus text and its author. Example: “A Wind from the North” by Bill Capossere.
• Analytical Focus.  Identify at least one part of the whole you’re studying. Example:  Repeated phrase “five or six days” (104).  Symbol – uncle’s car.  Motif – snow.
• Analytical Insight. Explain the function of that part in relationship to the whole. Example: They imply that living in isolation makes you lonely.
• Stakes . So what? Why does it matter? Example.: Sheds light on the fragility of life and the relationships we build throughout it.
• Consider adding …  A concession statement (“Although,” “even though,” etc.).  Example: Although there’s nothing wrong with preferring time alone, … OR  A question that you might pursue.  Example: Can Capossere’s uncle represent other isolated people?

THESIS:  Although there’s nothing wrong with preferring time alone, “A Wind from the North” by Bill Capossere sheds light on the fragility of life and the relationships we build throughout it. The text conveys the loneliness of an isolated lifestyle by symbolizing Capossere’s uncle with a “untouchable” car. Additionally, the narrator repeats images and phrases in the essay to reinforce his uncle’s isolation.

• Bloom, Benjamin S., et al. Taxonomy of Educational Objectives: The Classification of Educational Goals. D. McKay Co., 1969. ↵
• Also of note are recent emphases to use Bloom’s work as a conceptual model, not a hard-and-fast, infallible rule for cognition. Importantly, we rarely engage only one kind of thinking, and models like this should not be used to make momentous decisions; rather, they should contribute to a broader, nuanced understanding of human cognition and development. ↵
• In consideration of revised versions Bloom’s Taxonomy and the previous note, it can be mentioned that this process necessarily involves judgment/evaluation; using the process of interpretation, my analysis and synthesis require my intellectual discretion. ↵
• Mays 1258.Mays, Kelly J. “The Literature Essay.” The Norton Introduction to Literature, Portable 12th edition, Norton, 2017, pp. 1255-1278. ↵
• “Developing a Thesis.” Purdue OWL, Purdue University, 2014, https://owl.english.purdue.edu/owl/resource/616/02/. Reproduced in accordance with Purdue OWL policy and Creative Commons licensure.Read more advice from the Purdue OWL relevant to close reading at https://owl.english.purdue.edu/owl/section/4/17/ . ↵
• One particularly useful additional resource is the text “Annoying Ways People Use Sources,” externally linked in the Additional Recommended Resources appendix of this book. ↵
• Gallop 7. ↵

a cognitive and rhetorical process by which an author brings together parts of a larger whole to create a unique new product. Examples of synthesis might include an analytical essay, found poetry, or a mashup/remix.

a part or combination of parts that lends support or proof to an arguable topic, idea, or interpretation.

a 1-3 sentence statement outlining the main insight(s), argument(s), or concern(s) of an essay; not necessary in every rhetorical situation; typically found at the beginning of an essay, though sometimes embedded later in the paper. Also referred to as a "So what?" statement.

EmpoWord 111 Copyright © by Doug Bourne and Shane Abrams is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Analysis vs. Synthesis

What's the difference.

Analysis and synthesis are two fundamental processes in problem-solving and decision-making. Analysis involves breaking down a complex problem or situation into its constituent parts, examining each part individually, and understanding their relationships and interactions. It focuses on understanding the components and their characteristics, identifying patterns and trends, and drawing conclusions based on evidence and data. On the other hand, synthesis involves combining different elements or ideas to create a new whole or solution. It involves integrating information from various sources, identifying commonalities and differences, and generating new insights or solutions. While analysis is more focused on understanding and deconstructing a problem, synthesis is about creating something new by combining different elements. Both processes are essential for effective problem-solving and decision-making, as they complement each other and provide a holistic approach to understanding and solving complex problems.

Further Detail

Introduction.

Analysis and synthesis are two fundamental processes in various fields of study, including science, philosophy, and problem-solving. While they are distinct approaches, they are often interconnected and complementary. Analysis involves breaking down complex ideas or systems into smaller components to understand their individual parts and relationships. On the other hand, synthesis involves combining separate elements or ideas to create a new whole or understanding. In this article, we will explore the attributes of analysis and synthesis, highlighting their differences and similarities.

Attributes of Analysis

1. Focus on details: Analysis involves a meticulous examination of individual components, details, or aspects of a subject. It aims to understand the specific characteristics, functions, and relationships of these elements. By breaking down complex ideas into smaller parts, analysis provides a deeper understanding of the subject matter.

2. Objective approach: Analysis is often driven by objectivity and relies on empirical evidence, data, or logical reasoning. It aims to uncover patterns, trends, or underlying principles through systematic observation and investigation. By employing a structured and logical approach, analysis helps in drawing accurate conclusions and making informed decisions.

3. Critical thinking: Analysis requires critical thinking skills to evaluate and interpret information. It involves questioning assumptions, identifying biases, and considering multiple perspectives. Through critical thinking, analysis helps in identifying strengths, weaknesses, opportunities, and threats, enabling a comprehensive understanding of the subject matter.

4. Reductionist approach: Analysis often adopts a reductionist approach, breaking down complex systems into simpler components. This reductionist perspective allows for a detailed examination of each part, facilitating a more in-depth understanding of the subject matter. However, it may sometimes overlook the holistic view or emergent properties of the system.

5. Diagnostic tool: Analysis is commonly used as a diagnostic tool to identify problems, errors, or inefficiencies within a system. By examining individual components and their interactions, analysis helps in pinpointing the root causes of issues, enabling effective problem-solving and optimization.

Attributes of Synthesis

1. Integration of ideas: Synthesis involves combining separate ideas, concepts, or elements to create a new whole or understanding. It aims to generate novel insights, solutions, or perspectives by integrating diverse information or viewpoints. Through synthesis, complex systems or ideas can be approached holistically, considering the interconnections and interdependencies between various components.

2. Creative thinking: Synthesis requires creative thinking skills to generate new ideas, concepts, or solutions. It involves making connections, recognizing patterns, and thinking beyond traditional boundaries. By embracing divergent thinking, synthesis enables innovation and the development of unique perspectives.

3. Systems thinking: Synthesis often adopts a systems thinking approach, considering the interactions and interdependencies between various components. It recognizes that the whole is more than the sum of its parts and aims to understand emergent properties or behaviors that arise from the integration of these parts. Systems thinking allows for a comprehensive understanding of complex phenomena.

4. Constructive approach: Synthesis is a constructive process that builds upon existing knowledge or ideas. It involves organizing, reorganizing, or restructuring information to create a new framework or understanding. By integrating diverse perspectives or concepts, synthesis helps in generating comprehensive and innovative solutions.

5. Design tool: Synthesis is often used as a design tool to create new products, systems, or theories. By combining different elements or ideas, synthesis enables the development of innovative and functional solutions. It allows for the exploration of multiple possibilities and the creation of something new and valuable.

Interplay between Analysis and Synthesis

While analysis and synthesis are distinct processes, they are not mutually exclusive. In fact, they often complement each other and are interconnected in various ways. Analysis provides the foundation for synthesis by breaking down complex ideas or systems into manageable components. It helps in understanding the individual parts and their relationships, which is essential for effective synthesis.

On the other hand, synthesis builds upon the insights gained from analysis by integrating separate elements or ideas to create a new whole. It allows for a holistic understanding of complex phenomena, considering the interconnections and emergent properties that analysis alone may overlook. Synthesis also helps in identifying gaps or limitations in existing knowledge, which can then be further analyzed to gain a deeper understanding.

Furthermore, analysis and synthesis often involve an iterative process. Initial analysis may lead to the identification of patterns or relationships that can inform the synthesis process. Synthesis, in turn, may generate new insights or questions that require further analysis. This iterative cycle allows for continuous refinement and improvement of understanding.

Analysis and synthesis are two essential processes that play a crucial role in various fields of study. While analysis focuses on breaking down complex ideas into smaller components to understand their individual parts and relationships, synthesis involves integrating separate elements or ideas to create a new whole or understanding. Both approaches have their unique attributes and strengths, and they often complement each other in a cyclical and iterative process. By employing analysis and synthesis effectively, we can gain a comprehensive understanding of complex phenomena, generate innovative solutions, and make informed decisions.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

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Analysis and Synthesis Models ¶

Dft: analysis and synthesis ¶, analysis and synthesis ¶, plot figures ¶.

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Stft: analysis and synthesis ¶, plot figures: ¶.

FRFT: Fractional Fourier Transform ¶

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Sinusoidal Model: Analysis and Synthesis ¶

Audio output ¶

Original audio ¶.

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Reconstructed Audio ¶

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Residual Audio

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Title: program synthesis with large language models.

Abstract: This paper explores the limits of the current generation of large language models for program synthesis in general purpose programming languages. We evaluate a collection of such models (with between 244M and 137B parameters) on two new benchmarks, MBPP and MathQA-Python, in both the few-shot and fine-tuning regimes. Our benchmarks are designed to measure the ability of these models to synthesize short Python programs from natural language descriptions. The Mostly Basic Programming Problems (MBPP) dataset contains 974 programming tasks, designed to be solvable by entry-level programmers. The MathQA-Python dataset, a Python version of the MathQA benchmark, contains 23914 problems that evaluate the ability of the models to synthesize code from more complex text. On both datasets, we find that synthesis performance scales log-linearly with model size. Our largest models, even without finetuning on a code dataset, can synthesize solutions to 59.6 percent of the problems from MBPP using few-shot learning with a well-designed prompt. Fine-tuning on a held-out portion of the dataset improves performance by about 10 percentage points across most model sizes. On the MathQA-Python dataset, the largest fine-tuned model achieves 83.8 percent accuracy. Going further, we study the model's ability to engage in dialog about code, incorporating human feedback to improve its solutions. We find that natural language feedback from a human halves the error rate compared to the model's initial prediction. Additionally, we conduct an error analysis to shed light on where these models fall short and what types of programs are most difficult to generate. Finally, we explore the semantic grounding of these models by fine-tuning them to predict the results of program execution. We find that even our best models are generally unable to predict the output of a program given a specific input.

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• Published: 17 June 2024

An algorithmic framework for synthetic cost-aware decision making in molecular design

• Jenna C. Fromer   ORCID: orcid.org/0000-0003-2328-7457 1 &
• Connor W. Coley   ORCID: orcid.org/0000-0002-8271-8723 1 , 2  

Nature Computational Science ( 2024 ) Cite this article

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• Applied mathematics
• Drug discovery and development
• Lead optimization

A preprint version of the article is available at arXiv.

Small molecules exhibiting desirable property profiles are often discovered through an iterative process of designing, synthesizing and testing sets of molecules. The selection of molecules to synthesize from all possible candidates is a complex decision-making process that typically relies on expert chemist intuition. Here we propose a quantitative decision-making framework, SPARROW, that prioritizes molecules for evaluation by balancing expected information gain and synthetic cost. SPARROW integrates molecular design, property prediction and retrosynthetic planning to balance the utility of testing a molecule with the cost of batch synthesis. We demonstrate, through three case studies, that the developed algorithm captures the non-additive costs inherent to batch synthesis, leverages common reaction steps and intermediates, and scales to hundreds of molecules.

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Bayesian reaction optimization as a tool for chemical synthesis

SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules

Modeling the expansion of virtual screening libraries

Data availability.

SMILES and rewards used for all case studies 32 , 33 , 37 can be found at github.com/coleygroup/sparrow/tree/main/examples . All results can be reproduced using included configuration files in the same repository 52 . Source data are provided with this paper.

Code availability

SPARROW is open source and can be found at github.com/coleygroup/sparrow (ref. 52 ). All code and retrosynthetic routes from ASKCOS used to generate the described results can be found at github.com/coleygroup/sparrow/tree/main/examples . Full candidate sets with configuration files are included in this repository both for reproducibility and as examples for use of SPARROW.

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Acknowledgements

This work was supported by the DARPA Accelerated Molecular Discovery program (contract no. HR00111920025) and the Office of Naval Research (grant no. N00014-21-1-2195). J.C.F. received additional support from the National Science Foundation Graduate Research Fellowship (grant no. 2141064). We are grateful to M. Stenta, M. Pasquini, D. Jimenez and T. Ziegler for participating in discussions that guided the development of SPARROW. We are also grateful to M. A. McDonald, B. Koscher, R. Canty and the remaining authors of ref. 33 for providing the candidate set for case 2. Finally, we thank B. Mahjour and A. Zhang for providing insight into the validity of reactions and conditions proposed by retrosynthetic software.

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Jenna C. Fromer & Connor W. Coley

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C.W.C. and J.C.F. conceptualized the project, validated the method, analyzed results and wrote the paper. J.C.F. curated the data and wrote the software. C.W.C. supervised the work.

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Correspondence to Connor W. Coley .

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Nature Computational Science thanks Mingyue Zheng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team.

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Supplementary information

Supplementary information.

Supplementary Fig. 1 and Tables 1–4.

Supplementary Data 1

Starting material prices from the ChemSpace API in October 2023 and March 2024 used in the first case study, plotted in Supplementary Fig. 1a.

Supplementary Data 2

Starting material prices from the ChemSpace API in October 2023 and March 2024 used in the second case study, plotted in Supplementary Fig. 1b.

Source data

Source data fig. 3.

Numerical source data; reaction SMILES, scores and conditions

Source Data Fig. 4

Numerical source data for a–d

Source Data Fig. 5

Reaction SMILES, scores and conditions

Source Data Fig. 6

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Fromer, J.C., Coley, C.W. An algorithmic framework for synthetic cost-aware decision making in molecular design. Nat Comput Sci (2024). https://doi.org/10.1038/s43588-024-00639-y

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DOI : https://doi.org/10.1038/s43588-024-00639-y

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Analysis and synthesis model of compilation

By Team EasyExamNotes

The analysis and synthesis model of compilation helps bridge the gap between high-level programming languages and machine-level execution, enabling the development of efficient and portable software applications.

Analysis Phase:

The analysis phase focuses on understanding the structure and meaning of the source code, ensuring its correctness and adherence to syntax and semantics.

1. Lexical Analysis: The compiler breaks down the source code into individual tokens, such as keywords, identifiers, operators, and literals. It removes unnecessary elements like white spaces and comments.

2. Syntax Analysis: The compiler verifies the syntax of the code by checking the arrangement of tokens according to the language’s grammar rules. It builds a parse tree or abstract syntax tree (AST) that represents the hierarchical structure of the code.

3. Semantic Analysis: The compiler checks the meaning and context of the code. It ensures that expressions, statements, and declarations adhere to the language’s semantic rules. Type checking and symbol table construction are performed to catch any semantic errors.

Synthesis Phase:

The synthesis phase involves generating more efficient representations of the code, optimizing it, and finally producing the target code that can be executed by the computer.

1. Intermediate Code Generation: The compiler may generate an intermediate representation of the source code, which is often platform-independent and provides a more optimized representation for further processing.

2. Optimization: The compiler applies various optimization techniques to the intermediate code. These optimizations improve the efficiency and performance of the resulting executable. Examples include constant folding, loop unrolling, and dead code elimination.

3. Code Generation: The compiler generates the target code, which can be machine code specific to the target hardware or assembly language closely resembling the machine code. This output code is executable on the target system without the need for further translation.

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• L-attribute definition
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• Open access
• Published: 12 June 2024

An integrative review of the impact of allied health student placements on current staff’s knowledge and procedural skills in acute and primary care settings

• Mohammad Hamiduzzaman   ORCID: orcid.org/0000-0001-6027-1564 1 ,
• Sarah Miles   ORCID: orcid.org/0009-0007-5574-3409 1 ,
• Sarah Crook 1 ,
• Lewis Grove 1 ,
• Jennie Hewitt   ORCID: orcid.org/0000-0003-2736-005X 1 ,
• Frances Barraclough   ORCID: orcid.org/0000-0001-9230-7277 1 ,
• Peter Hawkins 1 ,
• Erika Campbell 1 ,
• Nicola Buster 1 ,
• Kate Thomson   ORCID: orcid.org/0000-0001-9661-299X 2 ,
• Christopher Williams   ORCID: orcid.org/0000-0001-8896-0978 1 &
• Vicki Flood   ORCID: orcid.org/0000-0001-5310-7221 1  

BMC Medical Education volume  24 , Article number:  657 ( 2024 ) Cite this article

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Staff shortages limit access to health services. The bidirectional benefits of allied health clinical placements are understood in the domains of student learning, health service delivery, and future workforce development. Still, the benefits to current workforce outcomes remain unknown. This review provides insights into the effects of allied health student placements in acute and primary care settings, particularly on healthcare staff's knowledge and procedural skills.

This search was based on the integrative review process established by Whittemore and Knafl in 2005. In October 2023, the first author (MH) searched five major electronic databases: Medline-EBSCO, PubMed, CINAHL, Embase, and Scopus. The CLUSTER model was used to track additional references. The first three authors (MH, SM, and SC) were involved in screening, quality appraisal, and synthesis of the studies. Data were thematically synthesised and analysed.

MeSH headings and keywords were used in key search areas: health education, health professional training, clinical placements, and allied health professions. The systematic search yielded 12 papers on allied health student placements across various healthcare settings in rural and metropolitan areas, with no high-quality methodologies measuring student placements' impact on staff knowledge and skills. Four main themes were identified from the analysis: meaningful student integration in service delivery, targeted educational support to healthcare staff, development of staff procedural skills and confidence, and the mechanisms of why student placements work in this aspect.

Conclusions

This review suggests that offering allied health student placement could be a promising approach to supporting rural healthcare staff in performing patient assessments and treatments proficiently and collaboratively. However, this requires further investigation to confirm.

Peer Review reports

Introduction

Healthcare staff shortages limit access to health services [ 1 ]. Four key areas for immediate attention in the Australian health context are food and nutrition, dementia care, the use of restrictive practices, and palliative care [ 2 ]. Allied health professionals have an important role to play in each of these areas. However, there is a critical shortage of allied health professionals and a higher turnover rate among allied health workers across Australia [ 2 , 3 ]. This shortage becomes more pronounced as the number of healthcare staff decreases with increasing remoteness [ 3 ]. Health service disparities persist between rural and metropolitan areas in Australia, with a gap in life expectancies (78 years compared to 82.5 years), a prevalence of chronic disease (21% vs 18% per 100,000 population), and potentially avoidable death rates (775.9 deaths vs 587.9 deaths per 100,000 population) [ 1 , 4 ]. Current funding and employment models have led to issues with recruitment and retention of allied health professionals and a shortage of staff [ 5 , 6 ]. For example, in 2018–19, only 29% of Australians used allied health services [ 7 ]. An additional challenge to upskilling healthcare staff is a lack of professional development opportunities [ 8 , 9 ]. Student placements have been identified as a potential approach for health workforce capacity building and support of health services delivery, especially in rural areas [ 9 , 10 , 11 ].

Various clinical training placement models exist to facilitate learning opportunities for medicine, nursing, and allied health students by integrating them into health service delivery for patients [ 12 ]. These placement models include practice-based learning [ 13 ], experiential learning [ 14 ], service-learning [ 15 ], work-integrated learning [ 16 ], and integrated clinical placements [ 17 ]. Clinical placements benefit students, educational institutions, and healthcare organisations in different ways, including personal growth and professional experience for students, academic rigour and service to the community for universities, and a workforce fit to practice in healthcare organisations. Evidence shows that clinical placements of students with exposure to acute and primary healthcare contexts are associated with better impacts in terms of students’ intellectual transformation [ 18 , 19 , 20 ], workforce capacity building [ 21 , 22 , 23 ], and patient health outcomes [ 24 , 25 ]. There remains a notable gap in research on allied health student placements that builds staff capability.

Educational and training resources designed for clinical supervision of allied health students during their placements can also serve as professional learning opportunities for healthcare staff. Professional development is imperative for healthcare staff to stay up to date with knowledge and technical skills and create innovative treatment planning. Complex and infrequently used clinical skills often deteriorate among health professionals, as confirmed in a systematic review by Main and Anderson [ 25 ] in Australia [ 26 ]. The National Health Workforce Strategy advocates for continuing professional education and training for health professionals so that professionals “maintain, improve, and broaden their knowledge, expertise, and competence, and develop the personal and professional qualities throughout their professional lives” [ 27 ]. Healthcare professionals have reported that ongoing education and training opportunities have improved their knowledge and procedural skills in client (e.g., patients, residents in aged care homes) care [ 28 ]. Since the COVID-19 pandemic, access to online professional development modules and training has improved [ 29 ]. However, a lingering question persists: can the co-creation of training programs and educational modules effectively contribute to the knowledge and skills development of both allied health students and healthcare staff?

A compelling association exists between student placements, health workforce capacity and capability building [ 30 ]. As noted earlier, student placements contribute to workforce recruitment and retention in rural and metropolitan areas by immersing them in health and social care settings. Throughout placements, students benefit from access to tutorials and clinical supervision [ 12 , 15 , 16 ]. Additionally, students and healthcare staff from different disciplines work collaboratively in a team during placements [ 31 ]. Pedagogical frameworks, including social learning theory [ 32 ], social constructivism [ 33 ], interprofessional learning [ 34 ], and community of practice [ 35 ] suggest that individuals working together learn with and from one another. The Royal Commission into Aged Care Quality and Safety in 2021 recommends strengthening allied health services [ 2 ], particularly in rural areas; therefore, a review of existing literature is important to inform how and why the placements work to enhance the capability of healthcare staff in service delivery.

Aims of the study

This review aims to synthesise the effects of allied health student placements on healthcare staff's knowledge and procedural skills in acute and primary care settings.

Two main questions guided this review:

Q1: How do the studies describe the integration of allied health students in services design and delivery in acute and primary care settings? Q2: How do these studies describe the effectiveness of allied health student placements for current healthcare staff’s knowledge and procedural skills in acute and primary care settings?

This review adhered to the five steps of an integrative review process as its foundation, established by Whittemore and Knafl in 2005 [ 36 ]. These steps included problem identification, literature search, data evaluation, data analysis, and presentation. We systematically searched the literature and employed the Mixed Method Appraisal Tool (MMAT) to assess the quality and rigour of the selected papers [ 37 ]. The extracted data were then analysed and presented thematically.

Search strategy

The systematic search for published documents was conducted following the PRISMA guidelines [ 38 ]. In October 2023, the first author (MH) searched five electronic databases: Medline-EBSCO, PubMed, Embase, CINAHL, and SCOPUS. A combination of MeSH headings and relevant concepts was used in crucial search areas: health education, health professional training, clinical placements, and allied health professions (the full search strategy is available in Table  1 ). The CLUSTER model was also employed to track sibling studies and citations for supplementary references.

Inclusion and exclusion criteria

The clinical placements are typically designed to immerse health students in real-life experience in acute and primary care settings with the aim of future workforce recruitment. Given the specific focus of this review on the impact of allied health student placements on the knowledge and procedural skills of existing healthcare staff, medical and nursing professions were not included in the search. The search was also limited to certain allied health disciplines based on the discussion with allied health clinicians and health service providers, such as physiotherapy, occupational therapy, dietetics, speech pathology, exercise physiology, social work, optometry, podiatry, psychology, and osteopathy. The inclusion criteria were articles and reports published in English, publication year 2001 to the present, descriptions of actual allied health student placements, and the placements aimed at enhancing the capacity and capabilities of current healthcare staff. Aligning with this review’s objectives and considering the scarcity of studies conducted in rural locations, the search was not restricted solely to rural placements. While the primary outcomes of allied health student placements predominantly centred on student learning, patient health and wellbeing, and workforce recruitment and retention, the studies that explored these aspects as their primary focus were not excluded when they identified the placements’ contribution to healthcare staff. Two reviewers, MH and HG independently screened the records retrieved by title, abstract, and full text. Discrepancies were discussed with a third reviewer, SM.

Quality appraisal

The MMAT criteria were used to assess the quality of studies, using a scale that spanned from 0, indicating no criteria met, to 5, indicating all criteria met, as detailed by Hong et al. in 2018. [ 37 ] To evaluate the studies, two reviewers, MH and HG, conducted separate assessments, allocating scores out of 5 (0—Unclear/No and 1: Yes). Through a consensus-driven process, it was determined that the papers included in this review exhibited a quality level that ranged from moderate (with a score of 3) to high (with a score of 5), as indicated in Table  2 .

Data extraction and analysis

Three reviewers, MH, SM, and SC, read the papers meeting the inclusion criteria multiple times to extract data. The extracted data were recorded separately by these three reviewers into Excel spreadsheets, with any discrepancies carefully cross-checked (Table  2 ). The extracted data included the study characteristics (author, year, country of origin, study design, study participants); characteristics of allied health student placements (placement setting, focus, participants, type of placement, the level of student involvement in service delivery); outcome data for existing healthcare staff’s knowledge and skills, as well as the limitations of these placements. Given that the selected studies were heterogeneous in methodologies, a thematic data synthesis was deemed the most appropriate approach [ 45 ]. The categories and sub-themes were independently identified by the reviewers, MH, SM, and SC, and were subsequently deliberated upon during review team meetings to determine the final themes and validate interpretations.

Figure  1 illustrates the selection process of the studies reviewed. Twelve papers that met the inclusion criteria represented the highest number over the past decade. Among these, eight studies used mixed methods for evaluating the placements, while two were qualitative and two were quantitative methodologies. The selected placements were mainly in Australia (10), with all papers originating from high-income countries, including the USA (1) and Canada (1). The healthcare settings were diverse across the placements; half were in residential aged care homes, while the rest were in hospitals, community health services, clinical skills centres, patient training centres, and non-government health organisations. The study participants included students, patients/residents, healthcare staff, health service managers, clinical educators, and relevant key stakeholders like family members and community organisations. Rural placement was reported in the majority of studies (7), but no studies compared the effects of different locations.

PRISMA 2020 flow diagram of systematic search and selection process

All twelve studies focused on either allied health student learning outcomes or service delivery across a range of settings by placing students. Most placement programs narrowly focused on the professional development of existing healthcare staff, while exclusive focus on this aspect was identified in four placement programs facilitated in hospitals, residential aged care homes, and community health services [ 39 , 47 , 49 , 50 ]. Undergraduate and postgraduate students from different allied health disciplines participated in the placements, including physiotherapy, occupational therapy, nutrition and dietetics, social work, and speech pathology. Some studies featured the collaboration between medicine, nursing, and allied health students [ 40 , 46 , 47 , 50 ]. Various types of placements were discussed, such as clinical placement [ 41 , 48 ]; work-integrated learning [ 42 ]; interprofessional team placement [ 40 , 43 , 49 ]; service-learning placement [ 39 , 44 , 47 , 50 ]; and simulated learning [ 51 ]. Interprofessional education was reported in most of the studies (8), and four studies provided information on the duration of placements, which ranged from four to ten weeks; in addition to detailing the types and focuses of the placements, the synthesis of outcome data revealed four key themes.

Meaningful student integration in service delivery

The integration of allied health students in health service delivery for patients was identified as a powerful and essential part of all placement programs. Student involvement in health service delivery was described by their engagement in a wide range of activities, from administration tasks and priority assessments to developing and implementing treatment plans and evaluating interventions. Eight studies reported direct engagement of students in developing treatment plans and designing and delivering services. Examples included person centred exercise programs, developing a sensory garden, implementing craft and cooking sessions for residents with dementia and training and upskilling care staff [ 39 , 40 , 44 , 46 , 47 , 48 , 49 , 50 ]. In contrast, four placement programs were restricted to organisations’ priority assessments [ 41 , 42 , 51 ]; shadowing a care worker and spending time with residents [ 43 ]; and planning and evaluation of interventions [ 41 , 42 , 51 ]. Student involvement in delivering direct health services to patients was identified in both urban and rural healthcare settings.

The extent of students’ involvement in delivering health services to patients was somewhat related to the degree to which the placement supported the capacity and capability building of existing healthcare staff. Integrating students in administrative tasks, priority assessments, and evaluation of the treatments contributed to staffing management and timely task completion, as well as a cultural shift towards collaboration among the staff [ 41 , 42 , 43 , 51 ]. Direct engagement of students in treatment plans and patient/resident care management was highly beneficial to a healthcare staff’s reflection and clinical reasoning [ 39 , 40 , 44 , 46 , 47 , 48 , 50 ]. Of note, none of the studies measured the causal relationships between the level of student integration in service delivery and the professional development of healthcare staff.

Targeted education support to healthcare staff

All studies reported that the placements led to an increase in knowledge, or had the potential to do so, for both students and healthcare staff. During these placements, various learning activities were offered to students, which, in turn, enhanced the knowledge of healthcare staff. For instance, learning activities like Grand Rounds and interprofessional education were implemented [ 44 , 46 , 47 , 50 , 51 ]. Key areas of learning for healthcare staff were identified in one evaluation study of interprofessional team placement in residential aged care homes [ 50 ], including mealtime positioning, post-stroke positioning, and medication management in palliative care. Additionally, one qualitative study described how the placements allowed healthcare staff to reorient themselves with the theories and methods behind the treatments [ 46 ]. Attending education and training sessions also helped the rural healthcare staff become familiar with the roles and responsibilities of other health disciplines [ 44 ].

Three studies reported that students generated new data and knowledge based on local evidence during their placements [ 41 , 42 , 50 ]. Two of the studies included rural placement of students [ 41 , 50 ], but all the studies confirmed that the students provided healthcare staff with current and innovative knowledge. This new knowledge supported the staff in strategic planning and prioritising patient assessments and treatments.

Development of staff procedural skills and confidence

Eight studies highlighted that allied health student placements were useful in developing procedural skills among healthcare staff. In four of these placements, student training sessions enhanced the healthcare staff’s efficiency in service delivery by reorienting them with the standards and procedures of the treatments [ 39 , 46 , 49 , 50 ]. Healthcare skills development various skills, including critical reflection, clinical reasoning, patient flow management, timely assessment and treatment of patients, continuity of care, clinical communication, patient safety, and evidence-based practice. The Delphi study conducted by MacBean et al. [ 43 ] in inpatient training centres in Australia provided insights into how the placements broaden the healthcare staff’s scope of practice in speech pathology, which was further complemented by the qualitative study of Kemp et al. [ 41 ] in Australian community health services. [ 42 , 51 ] Healthcare staff also gained confidence in performing clinical tasks during the student placements, with their abilities being questioned and affirmed [ 46 , 47 , 50 ]. Interprofessional team placements were found to be effective in two studies for team skills development [ 49 , 50 ]. Both rural and urban healthcare staff benefited equally from student placements in healthcare settings.

Why do student placements work? Insights into the mechanisms

This review identified the mechanisms underlying how the allied health student placements supported the professional development of healthcare staff in seven studies. While a cross-sectional study indicated non-statistically significant disadvantages of student placements in regional and rural residential aged care homes [ 48 ], six studies, spanning various healthcare settings, reported functional improvements in health service delivery attributed to student placements [ 39 , 40 , 42 , 46 , 49 , 50 ], regardless of the locations. These functional improvements in service delivery were because of additional training and resources, as well as active engagement in teaching, facilitating, and managing students within healthcare settings, which were identified as supportive for healthcare staff’s professional development [ 40 , 46 , 50 ]. Collaborative practice was found to be instrumental in reducing hierarchical culture among healthcare staff [ 43 , 49 ]. Additionally, the placements contributed to early patient readiness for discharge, providing staff with flexibility in using client care modalities, and questions from students increased staff awareness of evidence-based practice [ 39 , 50 ].

In order to facilitate discussions, the findings of this review are positioned within a general system theory framework (Fig.  2 ), enabling the assessment of inputs, transformational processes, outputs, and the environment within acute and primary healthcare settings.

Integration of allied health students in healthcare settings and its impact within a system theory framework

The role of allied health student placements in fostering professional development of healthcare staff is promising, with most of the studies in this review showing positive evidence. Service-based placements, with a meaningful integration of students in health service delivery, show the most potential. Service-based placements might work by offering Grand Rounds and interprofessional education sessions to healthcare staff in critical areas of client care, generating new knowledge that can form powerful local evidence, and enhancing healthcare staff's understanding of other health professionals and service providers that can promote the collaborative practice. Regardless of the locations, active engagement in supervising and educating students and increasing awareness of training sessions have proved to be beneficial for healthcare staff in developing their professional knowledge and skills in client care.

There is a strong evidence base for the integration of allied health students into various aspects of client care, but engagement has varied. Student involvement in service delivery can be particularly powerful as it primarily emphasises the improvement of patient accessibility and utilisation of health services that are otherwise not accessible to them, especially in rural communities [ 52 , 53 ]. In the studies included in this integrative review, students played vital roles in the development of treatment plans, treatment of patients, and evaluation of interventions, and this integration was found to be beneficial to current health workforce capacity and capability building. Previous placement programs involving medical and nursing students corroborate the positive outcomes, citing the development of confidence and proficiency in both students and healthcare staff [ 54 , 55 ]. These programs recognised the bi-directional benefits of clinical placements. Since 2021, the Rural Health Multidisciplinary Training (RHMT) in Aged Care Program has supported University Departments of Rural Health (UDRHs) in Australia to expand their capacity to facilitate health student placements in aged care settings. This review is timely to inform clinical educators by providing insights to design education sessions that meet the learning needs of students and staff.

Within the limited number of studies available for review , education sessions during student placements appear to be important for developing professional knowledge and skills of healthcare staff. This review strengthens the previous study findings in medicine and nursing placements in acute care settings, stating that Grand Rounds and interprofessional education opportunities increased healthcare staff and students’ awareness of different aspects of client care and expertise of their own and other professions [ 56 , 57 , 58 , 59 ]. These ongoing sessions cover various aspects of client care and are likely to equip staff with theories behind the treatments. Rural healthcare staff often have limited access to professional development opportunities, as well as supervision of students that has the potential to add a new perspective to the staff workloads [ 11 , 59 , 60 ]. Rural healthcare staff in community settings may also have limited time to engage with professional learning opportunities in their normal work routine, so embedding opportunities for ongoing education in the workplace through student placements may be beneficial. Opportunities must be explored in collaboration with healthcare and community partners to ensure professional development and training is co-designed and co-delivered to meet their staff’s unique needs. Creating ongoing learning opportunities for staff and engaging them in student supervision is vital to the success of placements.

In terms of creative learning, the student placements’ contribution to generating new and local evidence emerges with some supporting findings. Many studies explored how students are engaged in reciprocal learning relationships with peers and healthcare staff in the domains of clinical knowledge and procedural skills [ 58 , 61 ]. Students bring new or different perspectives, up-to-date knowledge of evidence-based practice, do not have the workload expectations, and are not restricted by funding requirements. This allows students to bring a different perspective. Students often have more time to complete projects and create resources, and when co-designed with staff and patients, such resources can enhance both staff learning and patient outcomes. However, these bi-directional learning benefits receive less attention from educators and rural health service providers. It may be unclear what students could add to the knowledge and skills of staff who are already registered and experienced in delivering services. Evidence is limited on how to design education sessions for different learner groups.

The review suggests that active engagement of healthcare staff is often absent in student placements. While clinical educators currently take the responsibility for student supervision and management, a potential improvement could involve active engagement of healthcare staff in these aspects during placements, which may help address the two remaining questions. First, whether it is important to create collaborative learning environments before offering student-led education of staff. This could enhance understanding and knowledge of both staff and student roles, increasing collegiality and co-design of learning and knowledge. A second question is whether adding a co-supervision role for healthcare staff in the allied health student placements (by adapting the models of medicine and nursing placements in rural communities) is a viable option to enhance staff engagement. This role could upskill the current health workforce in rural areas, increasing the capacity to take student placements. This role may combine rural knowledge with an understanding of student models and seek to implement changes in practices developed from student placements.

Limitations

Developing the search strategy was challenging because of the diversity in placements, disciplines, settings, and associated terminology. This resulted in a search that yielded only 12 eligible studies for review. Since allied health student placements in rural healthcare settings have expanded across high-income countries in recent years, there will likely be articles under review about unsuccessful placements that could have provided additional insights. Further rigorous investigations are required to strengthen the evidence surrounding student placements’ contribution to improving rural health staff knowledge and procedural skills in client care. These investigations could delve into the unique workforce outcomes associated with individual allied health disciplines and consider the different levels of study among students (undergraduate vs postgraduate).

This review is the first synthesis of the impact of allied health student placements on the professional development of our current health workforce. To enhance staff knowledge and skills and address shortages, particularly in rural and remote communities, this review indicates the importance of student integration in the delivery of health services. A collaborative learning approach to increase the knowledge of students and staff and improve staff engagement in placements that promote interprofessional learning is key to the professional development of current staff in any healthcare setting. While there is little evidence of the generation of new knowledge by students during their placements, there is no indication that these placements disadvantage healthcare staff in relation to their professional development. Clinical educators may consider establishing co-supervision roles for rural healthcare staff to foster interactions between staff and students and to enhance positive learning experiences for both parties. Individually tailored and co-designed professional development opportunities could be important, for instance, to assist rural healthcare staff in reducing adverse events and ensuring adequate health services and the quality of integrated care.

Availability of data and materials

All data generated or analysed during this study are included in this article.

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This study explores the challenges of integrating macro, meso, and micro in the articulation of advanced innovation policy and examines, respectively, dimensions of public business support, intermediary organizations, and knowledge transfer. It conducts an integrative review of the pertinent literature and a bibliometric analysis of 440 articles. It reveals three major obstacles that seemingly impede the effective integration of macro, meso, and micro in contemporary policymaking and socioeconomic analyses: entrenched boundaries between different thematic areas, methodological discrepancies, and the relative lack of integrated theoretical models. These factors contribute to the absence of unified functional hubs focused on microlevel interventions. The proposed Institutes of Local Development and Innovation (ILDIs) could mitigate these challenges as they are presented as multilevel policy instruments intended to provide support to businesses—particularly to those facing chronic and structural problems.

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Introduction

In an era where socioeconomic development is increasingly influenced by the interplay between different levels of analysis and policy implementation, understanding the dynamics between macro, meso, and micro becomes critical (Lanahan, 2016 ; Vlados & Chatzinikolaou, 2020b ). The current landscape highlights the urgency of integrating these levels to reinforce public support for businesses, effective intermediation, and sufficient knowledge transfer (Chen et al., 2023 ). This integration is essential for fostering a holistic approach to socioeconomic development, addressing both the overarching structural issues and the localized challenges faced by individual organizations (Zabala-Iturriagagoitia, 2022 ).

Past research has revealed “compartmentalization” problems in the mainstream socioeconomic discourse and policy approaches, critiquing the predominant focusing on either macroeconomic trends, mesolevel organizational frameworks, or microlevel individual behaviors. The seminal works of scholars like Nelson and Winter ( 1982 ), Galbraith ( 1987 ), and Dopfer et al. ( 2004 ) have underscored the entrenched boundaries among different thematic areas that have long hindered a comprehensive understanding of these interconnected levels. Methodological divergence, as highlighted by Barbour ( 2017 ), has further exacerbated a fragmented understanding, with different levels often requiring distinct research approaches. Α seeming absence of integrative, “macro-meso-micro,” Footnote 1 theoretical frameworks, as detailed by Zezza and Llambı́ ( 2002 ) and Mirzanti et al. ( 2015 ), likely leads to a noticeable void in policymaking, particularly in addressing the specific needs of less competitive microfirms, as indicated by Chatzinikolaou and Vlados ( 2022 ).

This paper aims to identify more clearly and address these gaps by conducting a thorough multilevel analysis of literature related to public business support, intermediary organizations, and knowledge transfer. It seeks to unravel the reasons for the limited integration of macro, meso, and micro in existing research and to identify new gaps that surface when these levels are collectively considered. The specific research questions are as follows:

What are the underlying reasons for the insufficient integration of macro, meso, and micro findings in research on public business support, intermediary organizations, and knowledge transfer?

What additional gaps arise from a multilevel synthesis of this literature, and why might a restructured macro-meso-micro approach be more adequate?

The subsequent sections of the paper are organized as follows: the second section introduces the concept of a macro-meso-micro synthesis and discusses the challenges in integrating these levels, focusing on the boundaries between different thematic categories and methods, along with the apparent absence of such holistic theoretical frameworks. The third section describes the methodology for the integrative review of 440 articles, detailing the literature search, inclusion criteria, bibliometric analysis, and critical evaluation. The fourth section presents the findings, exploring the apparent reasons behind the limited multilevel integration and examining specific macro-meso-micro policy frameworks as notable exceptions. The fifth section discusses these findings and their broader implications. The sixth section concludes the paper, emphasizing the necessity for integrated approaches in public support for business and policy formulation, and considers the potential of a policy proposal in addressing the identified gaps.

Theoretical Background

Macro-meso-micro synthesis.

The concept of an evolutionary macro-meso-micro synthesis provides a holistic framework for understanding socioeconomic development through interactions across various levels of perceptual and actual spatial scales. At least three significant obstacles seemingly hinder the effective integration of these levels—macro, meso, and micro—in current policymaking and socioeconomic analyses: entrenched boundaries across the various thematic categories, methodological differences, and the relative absence of integrated theoretical models.

The presence of “silos” among different thematic categories are likely an integration barrier. Grounded in evolutionary and institutional economics, the macro-meso-micro synthesis draws from different intellectual streams, challenging the traditional divide between microeconomics and macroeconomics (Galbraith, 1987 , pp. 295–297). It incorporates the mesolevel as a pivotal connector of individual and systemic behaviors, addressing the shortcomings in classical macroeconomic interpretations (Dopfer et al., 2004 , p. 68). Research such as that by Vlados and Chatzinikolaou ( 2020a , p. 115) on the ecosystems approach further illustrates the complex coevolutionary dynamics across these levels, moving beyond established notions of regional “growth poles” and industry “clusters.” However, different thematic categories often remain enclosed within their specific analytical boundaries—neoclassical and Keynesian economics often dwell on macrotrends, sociological studies probe mesoorganizational structures, and analyses of organizational behavior are the exclusive domain of microfactors (Nelson & Winter, 1982 ).

Advances in research methods, such as computational social science and big data analytics, hold promise for uncovering coevolving patterns across macro, meso, and micro (Barbour, 2017 , p. 10). Nevertheless, a conventional fragmentation in methods—quantitative for macrotrends and qualitative for microlevel case studies—complicates the integration of conclusions, pointing to the need for a dynamic mesolevel approach that bridges the macro and meso (Barbour, 2017 ).

The third barrier is the relative dearth of integrative, macro-meso-micro, theoretical frameworks. While the conversation surrounding macro-meso-micro policy integration is in its infancy, there have been significant contributions that highlight the need for multilevel socioeconomic interconnectivity Footnote 2 to effectively tackle a range of developmental and innovational challenges including rural poverty, entrepreneurial resilience, and competitiveness in the realm of integrated industrial policy. Zezza and Llambı́ ( 2002 p. 1881) emphasized the importance of embedding meso and micro into macro in terms of policymaking, suggesting a framework to assess their interplay within given environments. Mirzanti et al. ( 2015 , p. 407) illuminated the roles that individual entrepreneurs, organizations, and broader economic forces play across the spectrum of macro, meso, and micro, pinpointing essential conditions for entrepreneurial success such as psychological readiness, a culture of business innovation, and the fostering of competitive economies.

The triple helix theory, introduced by Etzkowitz and Leydesdorff ( 1995 ), serves as an integrative framework that suggests a synergistic relationship among universities, industries, and governments. This relationship underscores their interconnectedness and mutual influence in promoting innovation and wealth creation. Although the theory does not always explicitly employ a direct macro-meso-micro synthesis, this approach is implied. Etzkowitz ( 1996 ) further developed the framework, adopting an evolutionary perspective that connects the theory with regional innovation systems and proposes a coevolutionary relationship among institutional spheres.

As the theory evolved, it began to address the ongoing transition of innovation across socioeconomic organizations. Etzkowitz and Leydesdorff ( 1998 ) argued that such transitions do not have a fixed end point and expanded the concept to a global scale. However, critiques like those from Viale and Campodall’Orto ( 2002 ) have challenged the triple helix for its “fuzziness” and called for stronger interactions between academia and industry. Subsequent work by Etzkowitz and Leydesdorff ( 2000 ) further differentiated the triple helix from national innovation systems and mode 2 knowledge production, emphasizing its role in fostering interdisciplinary knowledge production structures.

The theory’s conceptual expansion continued with researchers such as Baber ( 2001 ) and Shinn ( 2002 ) exploring its implications for globalization and scientific disciplines. Notably, Carayannis and Campbell ( 2009 , 2010 ) expanded the framework into a quadruple and quintuple helix by incorporating media, culture, and environmental dimensions, thereby enhancing its applicability to sustainable development and social ecology. Carayannis et al. ( 2018 ) recently expanded the framework to the concept of regional coopetitive business ecosystems through the quadruple and quintuple helix models. They proposed that these ecosystems are multilevel configurations of tangible and intangible elements. They significantly enriched the relevant scientific debate by underlining the need for multilevel governance and corresponding strategic knowledge processes to enhance regional innovation.

In the vein of creating integrated industrial policies, Peneder ( 2017 , pp. 834–835) advanced the notion that effective policy must account for coevolution at all levels, with macrostrategies and mesodynamics collectively contributing to the attractiveness of a locale. Vlados and Chatzinikolaou ( 2020b , p. 8) went a step further with their “competitiveness web” concept, suggesting an ecosystem of interactions that unite firms, sectors, and economies into a cohesive, adaptive whole geared toward global competitive pressures. This concept delineates policy initiatives tailored to each level: microlevel policies targeting firm-specific advancements, mesolevel efforts aimed at invigorating regional innovation, and macroinitiatives designed to enact extended economic and societal reforms. Integral to this policy architecture are the “Institutes of Local Development and Innovation” (ILDIs), a policy proposal designed to synergize efforts between local governance, education systems, and businesses to bolster regional entrepreneurship and reinforce less competitive firms through a strategic six-step cycle (Chatzinikolaou & Vlados, 2022 ). This cycle begins with establishing a diagnostic system of the socioeconomic environment and progresses through the collection and synthesis of data, dissemination of local expertise, fostering innovation, enhancing local entrepreneurial capabilities, and culminates in the ongoing assessment of development outcomes.

Chatzinikolaou and Vlados ( 2022 ) detail how the suggested ILDIs can promote innovation through the “Stra.Tech.Man approach” (a blend of strategy, technology, and management), initially introduced by Vlados ( 2004 ). This approach compares firms to biological organisms, each with its unique “physiological” identity. Similar to how every organism possesses distinctive DNA that influences its growth and development path, each firm, regardless of size, has its unique foundational blueprint that guides its growth and development potential. The core of this approach is the Stra.Tech.Man synthesis, identifying three critical evolutionary aspects within which a firm operates: strategy, technology, and management. Though these aspects can be analyzed individually, they are intricately linked, forming the foundation of a firm’s capacity for innovative evolution, with the ILDIs positioned to bolster this innate potential.

Given that the macro-meso-micro analysis is still emerging, with only a few approaches identifiable through a preliminary literature review, it seems essential to bridge these gaps. Overcoming these challenges is vital for harnessing the complete potential of macro-meso-micro synthesis in advancing our understanding and promotion of socioeconomic development.

Public Support for Business, Intermediary Organizations, and Knowledge Transfer

Public support for business has long been a development staple, beginning with mercantilist policies in the sixteenth to eighteenth centuries where states bolstered domestic industries through subsidies and protective regulations (List, 1856 ). The nineteenth and twentieth centuries saw the advent of industrial policies to foster key sectors (Peneder, 2017 ), and post-WWII policies shifted focus to bolster small and medium-sized enterprises (SMEs), recognizing them as growth catalysts (Bianchi, 2000 ). Contemporary policies prioritize innovation and research, acknowledging their role in enhancing economic returns (Zabala-Iturriagagoitia, 2022 ), and have evolved in the aftermath of globalization to aid businesses in international competition (Chen et al., 2023 ). Modern strategies increasingly mesh public support with sustainable development and social goals (Meyer et al., 2019 ).

The role of intermediary organizations traces back to medieval educational and professional guilds, precursors to modern universities, and professional services firms that facilitate knowledge transfer (Kangas et al., 2013 ; Wright & Kipping, 2012 ). The late twentieth century saw universities commercializing research through technology transfer offices (Bessant & Rush, 1995 ), while business clusters and networks primarily disseminate knowledge within the different industries (Howells, 2006 ; Yusuf, 2008 ). The triple helix framework has also emerged in intermediating policy activities, emphasizing innovation synergy between industry, academia, and government (Betz et al., 2016 ; Carayannis & Morawska-Jancelewicz, 2022 ; Johnson, 2008 ), and the digital age has ushered in online platforms that link knowledge creators and consumers (Abi Saad et al., 2024 ).

Knowledge transfer is a practice rooted in human history, evolving from hands-on apprenticeships to codified knowledge in various forms (Heilbroner, 1963 ). Knowledge transfer and technology transfer are often interchangeable concepts because both involve the dissemination of skills, information, and innovations; however, knowledge transfer encompasses a broader range of strategies and management approaches, extending beyond the technical realm to include organizational learning and intellectual capital (W. M. Cohen & Levinthal, 1990 ; Nonaka & Takeuchi, 1995 ; Vlados, 2019 ). The internet has revolutionized knowledge accessibility (Gates, 1999 ), and companies are increasingly leveraging open innovation and external collaborations (Chesbrough, 2003 ). Advances in big data and AI are further transforming knowledge exchange, particularly in data-centric industries (Schwab, 2016 ). Footnote 3

However, these three elements—public support for business, intermediary organizations, and knowledge transfer—are often considered in isolation. Yet, there are notable cases where these dimensions have successfully converged, as seen with the UK’s TECs (Training and Enterprise Councils), the EU’s industrial strategy, and France’s “Pôles de compétitivité” (Boocock et al., 1994 ; Cohen, 2007 ; Mazzucato et al., 2015 ). These examples reflect an integrated approach across levels: nationally or regionally devised strategies at the macrolevel, regional tailoring at the mesolevel, and direct knowledge dissemination at the microlevel. Although the TECs have ceased to exist, their model toward direct SME reinforcement through advice services provides valuable lessons on integrated approaches—the TECs were the late 1980’s organic continuation of the past UK workforce organization, the “Manpower Services Commission.” Yet, beyond their legacy lies a subtle issue: current frameworks such as the EU industrial policy and France’s “Pôles de compétitivité” show few clear signs of significantly aiding less competitive microfirms. Footnote 4 This indicates a possible issue as these smaller entities, less competitive than their SME peers, may not be receiving the supportive framework required within these structures. This incongruity necessitates further exploration to comprehend and remedy the lack of support for these critical, yet vulnerable, sectors of the economy.

Methodology

We conducted an integrative literature review in the fields of public business support, intermediary organizations, and knowledge transfer (Torraco, 2005 ). This approach, inspired by Torraco ( 2005 ) and informed by recent reviews such as Andrikopoulos and Trichas ( 2018 ), Jugend et al. ( 2020 ), and Crișan et al. ( 2021 ), involved systematic analysis of selected literature. The methodology comprised four main stages: literature search, inclusion criteria determination, bibliometric review, and critical analysis.

For the literature review, the Scopus database was chosen due to its comprehensive coverage of over 20,000 social science journals and its ability to export bibliometric data. In contrast, the Web of Science database includes fewer social science journals, which justifies the preference for Scopus (Harzing & Alakangas, 2016 ). Three literature searches were conducted from October 26 to November 4, 2023, focusing on specific terms within article titles, abstracts, and keyword: 1) “public support and business,” 2) “intermediate organization or intermediary organization,” and 3) “knowledge transfer and entrepreneurship.” Additionally, terms were also searched in UK English and were enclosed in quotes for specific phrases (e.g., “public support”).

Regarding inclusion criteria, no date restrictions were applied. The search results were refined by subject area (business, management, and accounting), source type (journal articles), and language (English). The initial search yielded 117, 176, and 173 results for the respective search terms. After filtering for available full texts, the totals were 108, 170, and 164, leading to 440 unique articles as only two duplicates appeared in the intermediate organizations and knowledge transfer (Sampedro-Hernández & Vera-Cruz, 2017 ; Szulczewska-Remi & Nowak-Mizgalska, 2023 ). Notably, the search terms “public support and business,” as well as “knowledge transfer and entrepreneurship,” were used to ensure comparability in dataset sizes as the terms “knowledge transfer and business” yielded more than 1000 results. Supplementary File 1 presents these literature sets in a table and an APA-listed format.

The bibliometric review mapped the origins and evolution of these sets. It helped discern trends based on which journals were most prominent in the sample, along with the thematic categories of the contributing authors, as revealed through a word-frequency analysis—this aspect of the study is elaborated upon in the next section. Additionally, the full texts from all sets were combined into a single file, enabling a frequency analysis of specific words related to methods discussed in the literature. The data from this frequency analysis were then visualized in graphs using spreadsheets, offering a clear depiction of the evolving patterns. This analysis aided in identifying factors that contribute to integration challenges, a topic first introduced in the previous section and elaborated upon in the next section.

The study concluded with a qualitative critical analysis, identifying explicit and implicit macro-meso-micro policy frameworks in the literature. This analysis pinpointed certain gaps and led to coherent policy proposal recommendations presented in the next section.

Causes of Limited Integration

Boundaries between thematic categories.

The analysis of the entire sample reveals significant findings about the distribution of literature across journals in the three fields. Notably, journals like the “ Journal of Technology Transfer ,” “ Research Policy ,” and “ Technological Forecasting and Social Change ” stand out for their integrative scope, having published articles that encompass all three (Fig.  1 ).

Journal distribution (two documents or more)

The “ Journal of Technology Transfer ,” for example, exhibits a balanced distribution with five articles in public support for business, four in intermediary organizations, and 18 in knowledge transfer. “ Research Policy ” includes five in public business support, six in intermediary organizations, and eight in knowledge transfer. “ Technological Forecasting and Social Change ” displays two publications in public business support, nine in intermediary organizations, and nine in knowledge transfer. However, this multilevel trend is not widespread across the entire sample. Many journals have shown a preference or specialization in one field. Several journals, such as “ Corporate Ownership and Control ,” “ Economic and Industrial Democracy ,” and “ Futures ,” predominantly focus on a single field, typically intermediary organizations or knowledge transfer.

Only two publications, one by Sampedro-Hernández and Vera-Cruz ( 2017 ) on the role of entrepreneurs in the commercialization process through knowledge transfer intermediary organizations and another by Szulczewska-Remi and Nowak-Mizgalska ( 2023 ) on learning and entrepreneurship in the agricultural sector, appear in two of the fields. This fact highlights the ongoing challenges of integration in these.

In the analysis of specific term frequencies within the academic affiliations across the three sets, intriguing findings emerge. There appears to be a high concentration of certain terms within particular fields, indicating bounded integration among relevant thematic categories (Fig.  2 ).

Frequency of relevant terms in author affiliations

For instance, “economics” is mainly found in the “public business support” literature (30%), indicating a traditional focus on broader economic trends and policies. Conversely, “business” and “management” are prevalent in all areas, particularly in the “knowledge transfer” field (32% and 19%, respectively), reflecting a focus on individual firms and internal processes. Despite these challenges, some terms in academic affiliations appear more frequently than others in all three fields. The term “innovation” frequently appears across all levels, especially in the “intermediary organizations” context (10%), signifying the growing acknowledgment of innovation as a link between microlevel entrepreneurial actions, mesolevel organizations and networks, and macrolevel economic development and policy. Similarly, “technology” appears frequently in the contexts of meso (15%) and micro (9%), suggesting a cross-subject approach to technology studies. The distribution of terms like “enterprise,” “industrial,” and “entrepreneurship” across the three levels indicates some integrational trends. However, the dominance of terms like “social” in macro and “business” in micro points to apparent lack of integrative structure between relevant thematic categories.

In conclusion, the trends observed reflect seeming boundaries and siloed nature prevalent in some of the examined fields, with some exceptions where integration is evident. Notably, fields like innovation and technology show integration, but the specific focus of certain terms (e.g., “economics” in macrocontexts) underlines ongoing challenges. These findings underscore the need for more comprehensive theoretical frameworks and methodologies to facilitate cross-subject research and policymaking, especially in creating a cohesive macro-meso-micro synthesis.

Methodological Differentiation and Variations

An analysis of frequencies in certain terms related to research methods reveals distinct preferences in approaches, indicating varying degrees of the sought after integration. These frequencies were extracted after merging the full texts of the entire sample (Fig.  3 ).

Frequency of methodological terms

The term “regression” is used predominantly in macrocontexts (39%), indicating a preference for quantitative analysis at this level. In contrast, the mesolevel favors “interview” (57%), reflecting a qualitative, interpretive approach that focuses on individual or organizational perspectives. However, terms like “case study” and “qualitative” show a more balanced distribution, with higher frequencies in the micro (17% and 21%, respectively) and meso (17% and 13%, respectively) than in the macrocontext. This pattern suggests varying methodological integration across the three fields, particularly in efforts to understand and incorporate meso-micro into broader macroframeworks.

Macro-Meso-Micro Policy Frameworks

In the examined literature, an analysis concerning the application of a macro-meso-micro approach to policy points to notable instances, albeit few. These instances highlight ongoing integration challenges across the fields studied, while also suggesting the existence of certain valuable macro-meso-micro approaches worthy of attention.

Moreover, the literature review reveals instances where one-stop shops are effectively defined, like in Lambrecht and Pirnay ( 2005 ), which, despite offering valuable services, face challenges such as insufficient focus on SMEs’ unique qualitative needs. Liu et al. ( 2013 ) and Chen et al. ( 2015 ) describe entities that facilitate connections among small firms in bilateral or multilateral relationships, acting as agents in various innovation process aspects. Chen et al. ( 2023 ) note that these organizations are most effective when stemming from private-sector initiatives. While these approaches provide valuable information into the roles of intermediary organizations, they tend to overlook the integrative macro-meso-micro linkages.

However, there are initiatives in the examined literature that encompass various support mechanisms like grants, technology transfer, and research-development collaborations (Landry et al., 2013 ; Meyer et al., 2019 ; Mueller, 2023 ; Prodi et al., 2022 ; Sulej & Bower, 2006 ; Zabala-Iturriagagoitia, 2022 ). These programs primarily aim to foster an environment conducive to business innovation and growth, and we selectively present some important among them as follows:

At the macrolevel, initiatives like the US’ “Small Business Innovation Research” (SBIR) (Audretsch et al., 2002 ) and the Spanish “NEOTEC” program (Rojas & Huergo, 2016 ) have involved significant public sector actors, including federal ministries and national agencies. These actors have created policies and provided funds that aim to support a broad range of companies. However, these initiatives have often required the firms to have some level of existing capability or success, such as securing the first phase of SBIR funding (Lanahan, 2016 ) or having a developed technological potential.

Intermediating organizations, such as the Canadian Precarn (Johnson, 2008 ) and the Italian AREA Science Park (Battistella et al., 2023 ), have served to connect smaller firms with larger networks, providing access to specialized knowledge and fostering open innovation capabilities. These intermediary organizations have been crucial for the diffusion of innovation and for bringing together various stakeholders, including SMEs, universities, and research institutions. Nevertheless, the focus have often remained on entities that are already somewhat established and capable of engaging with these intermediary organizations. The Chinese Keyi Web (Liu et al., 2013 ) is an organization that has focused on MSMEs, though it remains unclear whether this one-stop shop has been established as part of a broader development policy, through private initiatives, or by both.

The transfer of core technology from academic institutions and the commercialization of federal research are among the main traits in relevant programs. They mainly indicate a focus on entities that are already engaged in innovation and technology development. Programs like UK’s “Knowledge Transfer Partnerships” (KTPs) (Wynn & Jones, 2019 ) and Canada’s iTMT (Abi Saad et al., 2024 ) have underscored the creation of supportive ecosystems and the encouragement of entrepreneurial activity, which are beneficial but may still not be tailored to the needs of the smallest and struggling firms.

We acknowledge a potential gap in supporting microfirms—particularly the less competitive ones. Most initiatives appear to assume a certain level of firm development and competitiveness, potentially neglecting firms not yet at this stage—Keyi Web being a possible exception (Liu et al., 2013 ). This indicates that while macro and meso receive attention through policy support and intermediation, the microlevel, particularly for less competitive firms, might not consistently receive the specialized, direct support necessary.

On a Restructured Synthesis of Development and Innovation Policy

The connection between development and innovation policy is not immediately apparent. Many macrolevel theorists often completely overlook innovation policy. It is usually more widely recognized at the mesolevel of different sectors or regions and at the microlevel by business theorists. However, effective development policy without innovation does not exist (Dosi, 1988 ; Edquist, 1997 ; Fagerberg et al., 2005 ; Freeman, 1987 ; Lundvall, 1992 ; Nelson & Winter, 1982 ; Schumpeter, 1942 ).

In response to this critique, we present the proposed policy of the Institutes of Local Development and Innovation (ILDIs), introduced in the Theoretical Background  section. We highlight their role in a broader context of business support, placing them within a structured ecosystem that integrates interventions at the macro, meso, and micro levels (Fig.  4 ).

The development and innovation policy ecosystem, as adapted from Vlados and Chatzinikolaou ( 2020b )

This proposed initiative builds on Vlados and Chatzinikolaou’s ( 2020b ) “competitiveness web” concept, advocating for an ecosystem of interactions that integrates firms, sectors, and other macrofactors. The macrolevel encompasses macrodynamic socioeconomic dimensions, from broader global dynamics to more specific demographic-environmental, cultural, and cognitive aspects. The formulation of national strategies to promote business support mechanisms must be at the forefront. This entails collaborative efforts between national ministries and agencies covering both macro and meso, creating a coopetitive environment that leads to mesolevel mechanisms for business support.

The mesolevel includes regional-local and sectoral dynamics, while the microlevel involves individual firms. It has been observed recently that actors and organizations that can contribute to business development remain quite uncoordinated, especially in less developed regional business ecosystems (Chatzinikolaou & Vlados, 2022 ; Rigg et al., 2021 ). These actors and organizations—which can be public, private, or mixed and arise from synergies among academic institutions, government interventions, and firms—include banks, business angels, venture capital firms, observatories, accelerators, technology transfer offices, coworking spaces, labor organizations, chambers of commerce, technology/science parks, incubators, and research centers (Bessière et al., 2020 ; Caird, 1994 ; Hackett & Dilts, 2004 ; Siegel et al., 2003 ). The ILDIs are proposed as key intermediate organizations in this ecosystem to coordinate these actors and organizations. Their proposed functions include the following:

Development observatory: Conducting continuous national-sectoral-local research to identify development prospects and periodical publishing of this intermediate organization’s outcomes.

Data analysis and synthesis: Facilitating collaborations among national and regional actors-organizations that potentially enhance entrepreneurship and evaluating investment opportunities for all involved socioeconomic organizations.

Local diffusion of expertise: Organizing business forums for knowledge exchange and dissemination of best practices.

Entrepreneurial “clinic”: Proposed as something different and more adaptable compared to other institutions like incubators and accelerators. It could offer free targeted consulting support to local organizations, especially those facing chronic and structural organizational problems—particularly in less-developed regional business ecosystems. This consulting could guide organizations to improve their business plans and subsequently achieve successful Stra.Tech.Man synthesis. This approach compares businesses to patients needing care as the ILDIs could “heal” long-term “ill” organizations—structurally deficient, relatively problematic, and lagging behind—and diagnose the prospects of healthier ones.

The specifics of how the ILDIs operate can be tailored to the individual ecosystemic needs. For instance, in a European context, they could establish offices in all NUTS-2 regions. The consulting process itself may also vary according to these tailored needs, possibly involving tax-funded private consultants to assist less competitive organizations in developing business plans. In essence, the ILDIs could function as a “developmental one-stop shop,” linking intermediate actors like an umbrella organization and providing public consultancy services to less competitive firms.

Τhe effectiveness of the ILDIs hinges on the acknowledgment that one-size-fits-all solutions are insufficient for addressing the diverse economic, political, cultural, and historical contexts globally. Ranging from the advanced economies in Northern Europe to the developing markets in the Balkans and Latin America, ILDIs require tailored strategies that consider the unique business ecosystems and intermediary-institutional structures of each region (Acemoglu & Robinson, 2012 ). The core of the ILDI model features the implementation of the Stra.Tech.Man approach, which integrates strategy, technology, and management into a unique mix that distinguishes it from traditional business support systems by acknowledging the specific “physiology” or unique “DNA” of firms, as stated earlier. This policy proposal is designed to employ an enhanced SWOT analysis that goes beyond standard approaches by emphasizing strategic flexibility and linking internal strengths and weaknesses with external opportunities and threats, utilizing principles of evolutionary economics (Vlados & Chatzinikolaou, 2019 ).

In conclusion, this integrative approach highlights complex ecosystemic relations in policy formulation, where government, industry, and academic institutions collaborate to monitor, analyze, and actively support business innovation and development. The proposal is sufficiently general to allow customization to specific national or international contexts, varying the degree of public support or intermediary actors. Political will is vital for implementing such wide-reaching interventions.

This integrative review revealed that previous research has not fully integrated findings from macro, meso, and micro regarding policies on public business support, intermediary organizations, and knowledge transfer, primarily due to boundaries among different thematic categories and certain methodological differences. The articles in the sample demonstrated varying degrees of multilevel integration, with many of them focusing on only some of the fields under study, thus perpetuating silos among different thematic categories. The analysis also highlighted certain aspects of methodological divergence contributing to the relative absence of cohesive macro-meso-micro synthesis, with macrolevel research favoring quantitative methods like regression and mesolevel research leaning more toward qualitative approaches such as interviews. Additionally, a notable gap was the insufficient focus on less competitive microfirms in policymaking. Besides their relatively infrequent appearance in the examined literature, most macro-meso-micro initiatives are primarily designed for firms at certain level of development, often overlooking the specialized needs of smaller or struggling entities. This underscored the need for more comprehensive and inclusive policies that cater to the unique requirements across all levels, promoting a holistic approach to public business support, intermediary organizations, and knowledge transfer.

This study aligns with past literature, emphasizing entrenched boundaries among different thematic categories and methods as relative barriers in integrating macro, meso, and micro in socioeconomic policymaking and analysis. It resonates with challenges identified by scholars like Galbraith ( 1987 ) and Dopfer et al. ( 2004 ) regarding the difficulty of transcending silos among thematic categories and methods. However, it diverges in recognizing the often-ignored needs of less competitive microfirms. Despite literature stressing multilevel interconnectivity for addressing broad socioeconomic challenges (Mirzanti et al., 2015 ; Zezza and Llambı́, 2002 ), it often neglects these smaller entities. This study shows that most policies fail to support these firms adequately. Introducing the ILDIs, it proposes an integrated policy approach, as advocated by Vlados and Chatzinikolaou ( 2022 ), focusing on the microlevel through a holistic macro-meso-micro framework.

Contrary to initiatives that solely concentrate on well-established companies or high-potential startups (cf. Mueller, 2023 ), the ILDIs advocate for an inclusive approach that encompasses businesses at different stages of development and across sectors, promoting a dynamic adaptation process and facilitating a deep understanding of the socioeconomic and entrepreneurial ecosystems they aim to enhance. Overall, the ILDIs are distinguished by three main characteristics: their explicit targeting of the microlevel, their holistic macro-meso-micro perspective, and their broad focus beyond merely developed businesses or emerging businesses with high potential, such as startups.

Most policy support approaches for businesses tend to be dichotomous (Vlados, 2004 ). On one hand, macrolevel strategies, like growth poles, focus on regional development in a traditional sense (Vlados & Chatzinikolaou, 2020a ). On the other hand, microlevel strategies, including incubators, knowledge and technology transfer offices, and accelerators, adopt a more limited business and management perspective. However, the main challenge in activating the proposed ILDIs lies in merging evolutionary economics with modern business management theories. The ILDI proposal marks a significant evolution from the triple helix model, drawing heavily on the work of Carayannis and Campbell ( 2009 , 2010 ) to highlight the model’s potential for acknowledging diverse macro-meso-micro interactions. Moreover, the ILDI approach aims to address various developmental and innovation bottlenecks in localized socioeconomic contexts.

This study, like all research, has certain limitations. Methodologically, the exclusion of related concepts like procurement and technology transfer may have narrowed the analysis. Additionally, focusing solely on public business support, intermediary organizations, and knowledge transfer overlooks other potentially relevant fields. Using exclusively Scopus and academic articles could also limit the study’s applicability to broader literature. In terms of conclusions, while the research acknowledges the challenges of integrating literature across macro, meso, and micro due to boundaries among different thematic categories and methodologies, it may underrepresent other complexities. These include potential academic funding biases toward narrow-against-multilevel research, disparities in data availability across different scales, and the challenges posed by varying temporal and spatial scales.

Future research opportunities arise from these limitations. An expanded integrative analysis could include additional terms and concepts similar to those used in this study. Future studies could also consider a broader range of literature sources, including books and reports, to enhance the scope and reliability of findings. Additionally, potential new research could reexamine the issues beyond boundaries due to different thematic categories and methodologies. This could involve exploring the inherent complexities in synthesizing data and theories across different scales, assessing the impact of academic funding biases on the scope of research, examining disparities in data availability at macro, meso, and micro, and addressing the challenges in reconciling the different temporal and spatial scales at which these levels operate.

In this study, we extensively explored the integration challenges of macro, meso, and micro in the context of public support for business, intermediary organizations, and knowledge transfer. We identified key barriers to integration, including entrenched boundaries among different thematic categories and methods. Despite these obstacles, we noted that some macro-meso-micro theoretical frameworks in the examined 440 articles indicate a gradual, albeit subtle, move toward integrated policy approaches. However, we also highlighted that these longstanding barriers have hindered the creation of effective policy frameworks that meet the needs at all three levels, especially for smaller, less competitive firms.

Main takeaways center around the need for holistic, integrated approaches in public support and policymaking. The introduction of the ILDIs policy proposal offers a promising avenue for addressing these challenges. The ILDIs, akin to hospitals providing care to the sick, could serve as developmental one-stop shops, offering tailored support to businesses. They could potentially bridge gaps between government, industry, and academia, fostering an environment of collaboration and support. This integrative approach could be adaptable to various national or international contexts, allowing customization based on the degree of public support and intermediary actors required.

From this point forward, the term macro-meso-micro will be used without quotation marks.

We focus only on the macro-meso-micro synthesis as a framework for multilevel analysis, although we recognize that there are other relevant approaches (Gonzalez et al., 2018 ). Multilevel analysis can perceive developments at the levels of niches, sociotechnical regimes, and exogenous sociotechnical landscapes (Geels 2011 ; Perez 2004 ). Niches are the origin of radical innovations. The sociotechnical regime refers to the established processes of specific sectors and includes institutions, infrastructures, and regulatory frameworks that provide not only stability but also resistance to change. The exogenous sociotechnical landscape includes external factors that affect niches and regimes. The “higher” hierarchical levels exhibit more stable relationships than the “lower” ones due to greater number of actors and alignment among the elements.

In a university setting, the effectiveness of knowledge transfer appears to be hierarchical, with the following channels ranked in descending order of importance as per Agrawal and Henderson ( 2002 ): formal consulting, scholarly publications (including journal articles and conference papers), the industry employment of graduates, research collaboration, co-supervision of students, obtaining patents and licenses, engaging in informal discussions, and delivering conference presentations.

Microfirms, with 0–10 employees and annual turnovers of up to 100,000 US dollars (or two million euros in the EU definition), along with other MSMEs (microfirms and small and medium-sized enterprises), play a crucial role in the global economy: they represent around two-thirds of employment in developed countries, nearly 99% of businesses in the EU, and significantly impact employment and GDP in developing countries, contributing to over 50% of employment and up to 40% of national income (Singh and Venkata 2017 ).

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Chatzinikolaou, D., Vlados, C. Public Support for Business, Intermediary Organizations, and Knowledge Transfer: Critical Development and Innovation Policy Bottlenecks. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-02161-y

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Adsorption of Ciprofloxacin on Graphene Oxide-Based Adsorbents: Synthesis, Characterization and DFT Calculations

• Sanchez, Sergio Nicolas Buitrago
• Spaolonzi, Marcela Pires
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In this article, the synthesis of graphene oxide from coal or coke and a composite graphene oxide-geopolymer were carried out to produce adsorbents applied to Ciprofloxacin (CIP) removal from water in a laboratory scale. The characterization analyzes of the adsorbent were performed by Thermogravimetric Analysis (TGA), Zeta Potential (ZP), Particle Size Distribution (PSD), X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Field Emission Gun Scanning Electron Microscopy (FEG/EDS), and RAMAN spectroscopy. Multilayer graphene oxide was identified after coal or coke thermal treatment and ultrasound exfoliation followed by ozone oxidation of the solid surface. A composite produced using phosphate mining residues and graphene oxide incorporation (22 wt%) showed the highest adsorption capacity. Affinity tests showed removal rates for CIP ranging from 15% to 55%. Kinetic studies indicated that the equilibrium time varied between 100 min and 120 min for three different initial CIP concentrations. With regard to the kinetic study, the pseudo-first order model better described the kinetics at the concentration of 0.05 mmol L -1 , while the pseudo-second order model better described the kinetics at concentrations of 0.1 mmol L -1 and 0.2 mmol L -1 . The studies on adsorption equilibrium showed that the Langmuir model provided the most accurate fit to the experimental data. The maximum adsorption capacity was found at 45 °C and it was of 0.24 mmol g -1 . The possible mechanisms of adsorption are related to electrostatic interactions and covalent bonds.

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