Canadian public health experiences during COVID-19: a new framework for assessing evidence
Bibliographic record
Abstract
Background: Public health emergencies like COVID-19 require public policy and practice decisions at a time of uncertainty and rapidly changing science. Methods: We conducted qualitative, phenomenological interviews with 25 senior Canadian public health leaders at local, provincial, and federal levels. Interviews explored how evidence was assessed, interpreted, and utilized during Canada's COVID-19 pandemic response. Data analysis followed rigorous inductive coding to identify key themes. Results: Participants highlighted limitations in traditional evidence hierarchies, emphasizing instead the critical role of timely, context-specific information such as predictive modeling, local surveillance data, and stakeholder insights. Officials described dynamically balancing methodological rigor with evidence credibility and applicability. We propose the Methodology-Credibility-Applicability (MCA) Evidence Framework, emphasizing simultaneous assessment across these three dimensions. Discussion: We document the experiences of public health leaders during the COVID-19 crisis, focusing on the assessment and use of evidence in decision making. The results challenge established hierarchies for assessing evidence and highlight the need for flexible, multidimensional frameworks for evaluating evidence during crises.
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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 | 0.234 | 0.298 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.047 | 0.028 |
| Science and technology studies | 0.034 | 0.080 |
| Scholarly communication | 0.045 | 0.020 |
| Open science | 0.010 | 0.026 |
| Research integrity | 0.007 | 0.010 |
| 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".