The application of knowledge synthesis methods in agri-food public health: Recent advancements, challenges and opportunities
Bibliographic record
Abstract
Knowledge synthesis refers to the integration of findings from individual research studies on a given topic or question into the global knowledge base. The application of knowledge synthesis methods, particularly systematic reviews and meta-analysis, has increased considerably in the agri-food public health sector over the past decade and this trend is expected to continue. The objectives of our review were: (1) to describe the most promising knowledge synthesis methods and their applicability in agri-food public health, and (2) to summarize the recent advancements, challenges, and opportunities in the use of systematic review and meta-analysis methods in this sector. We performed a structured review of knowledge synthesis literature from various disciplines to address the first objective, and used comprehensive insights and experiences in applying these methods in the agri-food public health sector to inform the second objective. We describe five knowledge synthesis methods that can be used to address various agri-food public health questions or topics under different conditions and contexts. Scoping reviews describe the main characteristics and knowledge gaps in a broad research field and can be used to evaluate opportunities for prioritizing focused questions for related systematic reviews. Structured rapid reviews are streamlined systematic reviews conducted within a short timeframe to inform urgent decision-making. Mixed-method and qualitative reviews synthesize diverse sources of contextual knowledge (e.g. socio-cognitive, economic, and feasibility considerations). Systematic reviews are a structured and transparent method used to summarize and synthesize literature on a clearly-defined question, and meta-analysis is the statistical combination of data from multiple individual studies. We briefly describe and discuss key advancements in the use of systematic reviews and meta-analysis, including: risk-of-bias assessments; an overall quality-of-evidence approach; engagement of stakeholders; Bayesian, multivariate, and network meta-analysis; and synthesis of diagnostic test accuracy studies. We also highlight several challenges and opportunities in the conduct of systematic reviews (e.g. inclusion of grey literature, minimizing language bias, and optimizing search strategies) and meta-analysis (e.g. inclusion of observational studies and approaches to address the insufficient reporting of data and significant heterogeneity). Many of these developments have yet to be comprehensively applied and evaluated in an agri-food public health context, and more research is needed in this area. There is a need to strengthen knowledge synthesis capacity and infrastructure at the regional, national, and international levels in this sector to ensure that the best available knowledge is used to inform future decision-making about agri-food public health issues.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.677 | 0.763 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.013 |
| Bibliometrics | 0.038 | 0.036 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.029 | 0.026 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".