Evaluating healthy cities: A scoping review protocol
Bibliographic record
Abstract
BACKGROUND: The Healthy Cities project supports municipal policymakers in the struggle to safeguard the health of urban citizens around the world (and in other limited geographies such as islands). Although Healthy Cities has been implemented in thousands of settings, no synthesis of implementation experiences have been conducted. In this article, we develop a scoping review protocol that can be applied to collect evidence on process evaluations of Healthy Cities. METHODS: To develop a scoping review protocol that could identify experiences evaluating the Healthy Cities project, we followed the PRISMA guidelines for Scoping Reviews (PRISMA-ScR). We applied these guidelines in consultation with a research librarian to design a search of the peer-reviewed literature, specifically Ovid Medline, Ovid Embase, Web of Science Core Collection, and Scopus databases, and a grey literature search. DISCUSSION: In addition to the aim of collecting evidence on Healthy Cities process evaluation experiences, the broader goal is to spark discussions and inform future evaluations of Healthy Cities. This work can also inform other evaluations of initiatives seeking to raise socio-political change, such as those focused on enhancing intersectoral and multisectoral action.
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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 | 0.244 | 0.228 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.074 | 0.020 |
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".