Connections between health research systems and decision-making spaces: lessons from the COVID-19 pandemic in the province of Québec, Canada
Bibliographic record
Abstract
The coronavirus 2019 (COVID-19) pandemic allowed for exceptional decision-making power to be placed in the hands of public health departments. Data and information were widely disseminated in the media and on websites. While the improvement of pandemic management is still a learning curve, the ecosystem perspective - that is, the interconnection of academic health research systems and decision-making spaces - has received little attention. In this commentary, we look at the mechanisms in place, or not, in Canada for ensuring decision-making spheres can "speak" to academic research systems. We look at the thick walls that are still in place between health research systems and decision-making spaces. More precisely, we discuss three organizational flaws that we identified in the evidence-informed decision-making ecosystem of Québec and, more broadly, Canada. We introduce some inspiring measures that other countries have implemented to better link evidence and public health decision-making during health crises. The observed flaws and options are related to the vitality of early information sharing relays, the cross-sectional capacity to issue opinions, and the collection and integration of hard and soft data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".