Concepts and approaches in the evaluation of health promotion
Bibliographic record
Abstract
The demands and tensions surrounding evidence-based policy (EBP) as part of results-based management have frequently indicated a gap between these concepts and the complex nature of health promotion interventions. This article discusses the challenges associated with the conceptual field of Health Promotion and the requirements for "proof" of effectiveness and efficiency faced by managers, evaluators, and local agents in the development of inter-sector health programs. The authors identify the limitations of epidemiological trials for the evaluation of social policies and use arguments related to "theories of change" in order to discuss the relationship of the "constructs" in the social policy intervention model and provide the basis for the "analysis of the contribution" of its effects. Systematic reviews of the "realist synthesis" type are discussed, due to their capacity for highlighting the theoretical framework of a specific program and explaining the underlying action mechanisms common to different programs and/or contexts. The authors argue that the expression and maintenance of expected social changes require the construction of collaborative processes, considering the set of (bottom-up) stakeholders involved in all stages of the process of developing and evaluating interventions.
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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 | 0.260 | 0.257 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.027 | 0.020 |
| Science and technology studies | 0.005 | 0.063 |
| Scholarly communication | 0.027 | 0.020 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".