Settings for Health Promotion: An Analytic Framework to Guide Intervention Design and Implementation
Bibliographic record
Abstract
Taking a settings approach to health promotion means addressing the contexts within which people live, work, and play and making these the object of inquiry and intervention as well as the needs and capacities of people to be found in different settings. This approach can increase the likelihood of success because it offers opportunities to situate practice in its context. Members of the setting can optimize interventions for specific contextual contingencies, target crucial factors in the organizational context influencing behavior, and render settings themselves more health promoting. A number of attempts have been made to systematize evidence regarding the effectiveness of interventions in different types of settings (e.g., school-based health promotion, community development). Few, if any, attempts have been made to systematically develop a template or framework for analyzing those features of settings that should influence intervention design and delivery. This article lays out the core elements of such a framework in the form of a nested series of questions to guide analysis. Furthermore, it offers advice on additional considerations that should be taken into account when operationalizing a settings approach in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.110 | 0.076 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".