Common Measures to Advance the Science and Practice of Population Intervention for Chronic Disease Prevention: The Promise and Two Early Experiences in Tobacco Control
Bibliographic record
Abstract
Abstract: Pertinent evidence to inform population interventions for chronic disease prevention is sparse. The use of common measures across multiple jurisdictions is a promising approach to study “natural experiments” that can dually advance research/knowledge development and evaluation/practice improvement for population intervention. Early experiences with provincial tobacco control strategies and North American quitlines reveal the importance of (a) sustained collaboration across research, evaluation, policy, and practice communities; (b) honouring different perspectives; and (c) stable institutional support for the creation and implementation of common measures. The promise of common measures will be better understood as mature examples of their use are explored.
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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 | 0.204 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".