What is the best set of indices/indicators for evaluating “health promotion governance” in health systems? A proposed methodology for health promotion decision-making
Bibliographic record
Abstract
We aimed to identify the indices/indicators needed for assessing health promotion governance in health systems. Data were obtained from a literature review, a modified Delphi process, and an Analytic Hierarchy Process (AHP). The study was conducted from May 2021 to January 2022 using a multi-method design comprised of four sequential stages: (1) a comprehensive literature review for identifying the health promotion indices/indicators originating from the Ottawa Charter’s action areas; (2) a 3-round Delphi survey to identify, by consensus, which of those indices/indicators would be relevant to the health promotion governance in the health system;(3) Analytic Hierarchy Process technique (AHP) to prioritize the selected indices/indicators; (4) presenting the results of AHP to a group of the health promotion specialists of the selected experts to selecting other indices/indicators if necessary. Health literacy was considered the most important index to assess health promotion governance from experts’ perspectives. The per capita public health expenditure was supposed to be the second most important indicator. The indices ranked third and fourth in priority were the health equality and urban health indexes, respectively. The health promotion specialists selected three other indices: the number of policies changed due to advocacy, social capital, and positive health. Based on the results, a series of essential indices/indicators have been proposed depending on the study’s purpose, which can help health system policymakers and managers to assess the health promotion governance in the health system.
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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 | 0.101 | 0.091 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 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".