The evolution and adaptation of evidence synthesis during the COVID-19 pandemic in Canada: Perspectives of evidence synthesis producers
Bibliographic record
Abstract
The demand for evidence syntheses to inform urgent decision-making surged during the pandemic. The challenging circumstances of the pandemic created significant hurdles for both those requesting and creating evidence syntheses, leading to the refinement and adjustment of evidence synthesis practices. This research sought to capture and explore how the field of evidence synthesis evolved and adapted during the pandemic from the perspective of those who produced evidence syntheses in Canada. In this qualitative study, semi-structured interviews were carried out between October 2022 to January 2023. Twenty-two participants from 19 different organizations across seven provinces and one territory were interviewed. This included producers of evidence syntheses from academic institutions, not-for-profit organizations, and provincial and federal government. Data analysis was conducted thematically using a phenomenological approach. Results indicated the evidence synthesis landscape drastically changed during the pandemic including short timelines to produce syntheses and changes in the volume, types, and quality of literature included in them. Due to the changing landscape and different needs of requestors, evidence synthesis methodologies evolved, synthesis products were tailored, and quality assessment tools were adapted. In addition, the use of artificial intelligence, processes for engaging subject matter experts and patient-citizen partners, and the coordination of the evidence synthesis community changed. The findings of this study contribute to the ongoing dialogue surrounding evidence synthesis to inform decision-making, and highlights the importance of flexibility and necessity of continuously evolving methodologies to meet the demands of frequently changing landscapes. The lessons learned from this study can help inform future strategies for improving evidence synthesis practices not only in the face of public health emergencies, but also in everyday practice.
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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 | 0.506 | 0.476 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.044 | 0.061 |
| Scholarly communication | 0.054 | 0.017 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".