Status report - Identifying equity-focussed interventions to promote healthy weights
Bibliographic record
Abstract
INTRODUCTION: We developed screening criteria to identify population health interventions with an equity focus for inclusion on the Public Health Agency of Canada's Canadian Best Practices Portal. We applied them to the area of "healthy weights," specifically, obesity prevention. METHODS: We conducted a review of the literature and obtained input from expert external reviewers on changes to midstream environments. Interventions had to identify outcomes for groups with an underlying social disadvantage. We included papers with a focus on equity and vulnerable populations, intervention and/or evaluation studies, social determinants of health and healthy weights or obesity prevention. We then appraised the shortlisted studies for quality of evidence to determine eligibility for inclusion as promising practices on the Canadian Best Practices Portal. RESULTS: Few of the references reviewed passed the equity screening criteria (26 out of 2823 published papers reviewed, or 0.9%). Six (of the 26) interventions qualified as promising practices. CONCLUSION: The ability of the equity screening criteria to distinguish midstream-level interventions for obesity prevention suggests that the criteria have potential to be applied to other public health topics. What is most important about our work is that the Portal, which is no longer being updated but is still accessible, was broadened to include interventions with a focus on equity.
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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 | 0.067 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".