A Guide for Developing Plain-Language and Contextual Summaries of Systematic Reviews in Agri-Food Public Health
Bibliographic record
Abstract
The application of systematic reviews is increasing in the agri-food public health sector to investigate the efficacy of policy-relevant interventions. In order to enhance the uptake and utility of these reviews for decision-making, there is a need to develop summary formats that are written in plain language and incorporate supporting contextual information. The objectives of this study were (1) to develop a guideline for summarizing systematic reviews in one- and three-page formats, and (2) to apply the guideline on two published systematic reviews that investigated the efficacy of vaccination and targeted feed and water additives to reduce Salmonella colonization in broiler chickens. Both summary formats highlight the key systematic review results and implications in plain language. Three-page summaries also incorporated four categories of contextual information (cost, availability, practicality, and other stakeholder considerations) to complement the systematic review findings. We collected contextual information through structured rapid reviews of the peer-reviewed and gray literature and by conducting interviews with 12 topic specialists. The overall utility of the literature searches and interviews depended on the specific intervention topic and contextual category. In general, interviews with topic specialists were the most useful and efficient method of gathering contextual information. Preliminary evaluation with five end-users indicated positive feedback on the summary formats. We estimate that one-page summaries could be developed by trained science-to-policy professionals in 3-5 days, while three-page summaries would require additional resources and time (e.g., 2-4 weeks). Therefore, one-page summaries are more suited for routine development, while three-page summaries could be developed for a more limited number of high-priority reviews. The summary guideline offers a structured and transparent approach to support the utilization of systematic reviews in decision-making in this sector. Future research is necessary to evaluate the utility of these summary formats for a variety of end-users in different contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.073 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".