Frameworks, theories and models used in the development of health policies: A systematic review of systematic reviews
Bibliographic record
Abstract
BACKGROUND: Health policies are established to address a specific health need, however, are not always the result of a rational process of evaluation or developed using established policy frameworks, theories or models (FTMs). Greater utilisation of FTMs in health policy making may provide further insight into policy development and overcome barriers to policy inaction. OBJECTIVE: The present review aimed to analyse the FTMs and their components underpinning health policy development, and health settings to which they are applied. METHOD: A systematic review was conducted following the PRISMA guidelines. Several databases were searched using keywords and MeSH terms. Quality appraisal was undertaken using the AMSTAR tool. RESULTS: From 1059 citations, 18 systematic reviews were identified. Twenty-eight FTMs were identified with 15 key components, with policy actors (85 %) and policy context (71 %) being most frequently mentioned. Policy FTMs were applied predominantly in health equity, population and public health (n = 16), sexual, reproductive and women's health (n = 14), HIV (n = 12), and physical activity, obesity prevention and nutrition (n = 12). CONCLUSION: The utilisation of health policy FTMs in the development of health policy may allow more targeted and relevant health policies to be developed. Further research into the critical components of health policy making may assist in developing a policy framework specific to health policy development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.073 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".