Health in All Policies utilization by municipal governments: scoping review
Bibliographic record
Abstract
The aim of this scoping review was to examine the utilization of a Health in All Policies (HiAP) approach in municipal government settings. Specific objectives included: to review peer reviewed and grey literature, to identify common themes from the literature, and to highlight gaps in the evidence base for HiAP. An iterative scoping review method was used. Documents were identified through searches of academic databases, reference lists and journal indices, and the World Wide Web. Included documents focused on HiAP in the local or municipal government context, published in English, between 2006 and 2015. Data were extracted and analyzed using descriptive statistics and a narrative thematic method. As of June 2015, 26 documents met the inclusion criteria. A lack of research studies examining HiAP in the municipal government context was identified. Three broad themes were abstracted from analysis of the documents: the conceptualization of HiAP, the adoption of HiAP, and the implementation of HiAP. The focus on a HiAP approach at the municipal level of government is growing. A majority of the existing documents provide narrative evidence and recommendations for implementing a HiAP approach at the municipal level. Research is needed in the areas of conceptualization, implementation, adoption and evaluation of a HiAP approach in municipal settings.
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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 | 0.059 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.026 | 0.045 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".