The production and utility of evidence synthesis during the COVID-19 pandemic in Canada: perspectives of evidence synthesis producers
Bibliographic record
Abstract
Background: COVID-19 accentuated an evergreen dilemma in evidence-informed policy making: the imperative to synthesise the best available evidence with limited time to produce high quality synthesis. The pandemic prompted the adaptation of evidence synthesis practices to match the urgency of the crisis, and heightened demand by policy makers, while maintaining a focus on quality. This study documents the response to these challenges from the perspectives of those who produced evidence syntheses in Canada. Methods: A qualitative phenomenological study was conducted between October 2022 and January 2023. Data collection included interviews with 22 participants within 19 organisations across seven provinces. A thematic analysis was performed and reported narratively. Results: Evidence synthesis producers in Canada adapted in response to the demands of different types of requests during the pandemic. Participants described several key challenges in responding to end-users, in which a lack of knowledge of evidence synthesis processes and products prompted difficult questions and unrealistic expectations. They responded to the needs of evidence synthesis requestors by creating custom syntheses, utilising rapid review methodologies, emphasising limitations and incorporating recommendations into syntheses. Discussion and conclusion: The evidence synthesis field was able to adapt to pandemic challenges in valuable ways. Still, this experience accentuates disconnects between producers and users, including differing views on the purpose, methods, limitations and implementation of synthesis findings.
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 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 | 0.593 | 0.655 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.022 |
| Science and technology studies | 0.031 | 0.053 |
| Scholarly communication | 0.053 | 0.014 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".