Settings for Health Promotion: Linking Theory and Practice
Bibliographic record
Abstract
The Settings Approach to Health Promotion - Lawrence W Green, Irving Rootman and Blake D Poland Home and Families as Health Promotion Settings - Hassan Soubh and Louise Potvin Commentary - Lawrence Fisher Commentary - Ilze Kalnins The School as a Setting for Health Promotion - Guy Parcel, Steven Kelder and Karen Basen-Enquist Commentary - Cheryl Perry Commentary - Peter McLaren, Zeus Leonardo, Xochitl Perez Promoting the Determinates of Good Health in the Workplace - Michael Polanyi et al Commentary - Robert Bertera Commentary - Joan Eakin The Health Care Institutions as Settings for Health Promotion - Joy Johnson Commentary - Jane Lethbridge Commentary - Patricia Mullen and L Kay Bartholomew Health Promotion in Clinical Practice - Vivek Goel and Warren McIsaac Commentary - David Butler-Jones Commentary - Jane Zapka Community as a Setting for Health Promotion - Marie Boutilier, Shelley Cleverly and Ronald Labonte Commentary - John Raeburn Commentary - Evelyn deLeeuw The State as a Setting - John Lavis and Terrence Sullivan Commentary - Marshal Kreuter Reflections on Settings for Health Promotion - Blake D Poland, Lawrence W Green and Irving Rootman
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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 | 0.022 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.079 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".