Indices/Indicators Developed to Evaluate the “Creating Supportive Environments” Mechanism of the Ottawa Charter for Health Promotion: A Setting-Based Review on Healthy Environment Indices/ Indicators
Bibliographic record
Abstract
This study aimed to identify the indices/indicators used for evaluating the "creating supportive environments" mechanism of the Ottawa Charter for Health Promotion, with a focus on built environments, in different settings. A search for literature with no time limit constraint was performed across Medline (via PubMed), Scopus, and Embase databases. Search terms included "Ottawa Charter," "health promotion," "supportive environments," "built environments," "index," and "indicator." we included the studies conducted on developing, identifying, and/or measuring health promotion indices/indicators associated with "built environments" in different settings. The review articles were excluded. Extracted data included the type of instrument used for measuring the index/indicator, the number of items, participants, settings, the purpose of indices/indicators, and a minimum of two associated examples of the indices domains/indicators. The key definitions and summarized information from studies are presented in tables. In total, 281 studies were included in the review, within which 36 indices/indicators associated with "built environment" were identified. The majority of the studies (77%) were performed in developed countries. Based on their application in different settings, the indices/indicators were categorized into seven groups: (1) Healthy Cities (n=5), (2) Healthy Municipalities and Communities (n=18), (3) Healthy Markets (n=3), (4) Healthy Villages (n=1), (5) Healthy Workplaces (n=4), (6) Health-Promoting Schools (n=3), and (7) Healthy Hospitals (n=3). Health promotion specialists, health policymakers, and social health researchers can use this collection of indices/indicators while designing/evaluating interventions to create supportive environments for health in various settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.035 | 0.032 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".