A Systematic Map of Non-Clinical Evidence Syntheses Published Globally on COVID-19
Bibliographic record
Abstract
STUDY OBJECTIVE: Evidence syntheses perform rigorous investigations of the primary literature and they have played a vital role in generating evidence-based recommendations for governments worldwide during the Covid-19 pandemic. However, there has not yet been an attempt to organize them by topic and other characteristics. This study performed a systematic mapping exercise of non-clinical evidence syntheses pertaining to Covid-19. METHODS: This study conducted a systematic search on December 5, 2020 across 10 databases and servers: CINAHL (EBSCO Information Services, Ipswich, Massachusetts, United States), Embase (Elsevier, Aalborg, Denmark), Global Health (EBSCO Information Services, Ipswich, Massachusetts, United States), Healthstar (NICHSR and AHA, Bethesda, United States), MEDLINE (NLM, Bethesda, United States), PsychINFO (APA, Washington, DC, United States), Web of Science (Clarivate Analytics, London, UK), Research Square (Research Square, Durham, North Carolina), MEDRxiv (Cold Spring Harbor Laboratory, New York, United States), and PROSPERO (NIHR, York, United Kingdom). Only full evidence syntheses published in a peer-reviewed journal or preprint server were included. RESULTS: This study classified all evidence syntheses in the following topics: health service delivery (n = 280), prevention and behavior (n = 201), mental health (n = 140), social epidemiology (n = 31), economy (n = 22), and environment (n = 19). This study provides a comprehensive resource of all evidence syntheses categorized according to topic. CONCLUSIONS: This study proposes the following research priorities: governance, the impact of Covid-19 on different populations, the effectiveness of prevention and control methods across contexts, mental health, and vaccine hesitancy.
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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 | 0.119 | 0.425 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.205 | 0.156 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".