Emerging Lessons from Health Systems and Policy Reforms during COVID-19
Bibliographic record
Abstract
Focusing events — sudden, relatively uncommon events that can be reasonably defined as harmful or portending of greater future harms (Birkland 1998), such as infectious disease pandemics — can push problems onto decision-making agenda leading policy-makers to formulate and adopt responses. Occasionally, in the process of responding to such crises, policy-makers also address long-standing related or tangential problems because they have come to understand the old problems in new or different ways, additional stakeholders are lobbying to address the lingering issues, or because a window has finally opened to make change (Kingdon 1995). [continued in PDF / HTML] Les événements déterminants — des événements soudains, relativement rares, que l'on peut raisonnablement définir comme dommageables ou annonciateurs de dommages futurs plus importants (Birkland 1998), tels que les pandémies de maladies infectieuses — peuvent mettre les problèmes à l'ordre du jour de la prise de décision, amenant les décideurs politiques à formuler et à adopter des réponses. Parfois, dans le processus de réponse à ces crises, les décideurs politiques s'attaquent également à des problèmes connexes ou tangentiels de longue date parce qu'ils en sont venus à comprendre les anciens problèmes d'une manière nouvelle ou différente, parce que d'autres parties prenantes font pression pour traiter les problèmes persistants ou parce qu'une fenêtre s'est enfin ouverte pour opérer un changement (Kingdon 1995). [suite en PDF / HTML]
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".