Between worst and best: developing criteria to identify promising practices in health promotion and disease prevention for the Canadian Best Practices Portal
Bibliographic record
Abstract
INTRODUCTION: In health promotion and chronic disease prevention, both best and promising practices can provide critical insights into what works for enhancing the healthrelated outcomes of individuals and communities, and how/why these practices work in different situations and contexts. METHODS: The promising practices criteria were developed using the Public Health Agency of Canada's (PHAC's) existing best practices criteria as the foundation. They were modified and pilot tested (three rounds) using published interventions. Theoretical and methodological issues and challenges were resolved via consultation and in-depth discussions with a working group. RESULTS: The team established a set of promising practices criteria, which differentiated from the best practices criteria via six specific measures. CONCLUSION: While a number of complex challenges emerged in the development of these criteria, they were thoroughly discussed, debated and resolved. The Canadian Best Practices Portal's screening criteria allow one to screen for both best and promising practices in the fields of public health, health promotion, chronic disease prevention, and potentially beyond.
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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 | 0.108 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".