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Record W1479127204 · doi:10.1089/fpd.2014.1807

A Guide for Developing Plain-Language and Contextual Summaries of Systematic Reviews in Agri-Food Public Health

2014· article· en· W1479127204 on OpenAlexafffund
Ian Young, Ashley Kerr, Lisa Waddell, Mai Pham, Judy Greig, Scott A. McEwen, Andrijana Rajić

Bibliographic record

VenueFoodborne Pathogens and Disease · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of GuelphPublic Health Agency of Canada
FundersMinistry of Rural AffairsPublic Health AgencyUniversity of GuelphCanadian Foundation for Healthcare Improvement
KeywordsPlain languageGrey literatureSystematic reviewPsychological interventionGuidelineStakeholderSocial mediaComputer scienceKnowledge translationMedical educationMEDLINEMedicineKnowledge managementPublic relationsWorld Wide WebNursingPathologyPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.073
metaresearch head score (Gemma)0.116
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0730.116
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.607
GPT teacher head0.472
Teacher spread0.134 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2014
Admission routes2
Has abstractyes

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