The GREENH-City interventional research protocol on health in all policies
Bibliographic record
Abstract
BACKGROUND: This paper presents the research protocol of the GoveRnance for Equity, EnviroNment and Health in the City (GREENH-City) project funded by the National Institute for Cancer (Subvention N°2017-003-INCA). In France, health inequities have tended to increase since the late 1980s. Numerous studies show the influence of social, economic, geographic and political determinants on health inequities across the life course. Exposure to environmental factors is uneven across the population and may impact on health and health inequities. In cities, green spaces contribute to creating healthy settings which may help tackle health inequities. Health in All Policies (HiAP) represents one of the key strategies for addressing social and environmental determinants of health inequities. The objective of this research is to identify the most promising interventions to operationalize the HiAP approaches at the city level to tackle health inequities through urban green spaces. It is a participatory interventional research to analyze public policy in real life setting (WHO Healthy Cities). METHOD/DESIGN: It is a mixed method systemic study with a quantitative approach for the 80 cities and a comparative qualitative multiple case-studies of 6 cities. The research combines 3 different lens: 1/a political analysis of how municipalities apply HiAP to reduce social inequities of health through green space policies and interventions 2/a geographical and topological characterization of green spaces and 3/ on-site observations of the use of green spaces by the inhabitants. RESULTS: City profiles will be identified regarding their HiAP approaches and the extent to which these cities address social inequities in health as part of their green space policy action. The analysis of the transferability of the results will inform policy recommendations in the rest of the Health City Network and widely for the French municipalities. DISCUSSION/CONCLUSION: The study will help identify factors enabling the implementation of the HiAP approach at a municipal level, promoting the development of green spaces policies in urban areas in order to tackle the social inequities in health.
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 | 0.136 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.156 | 0.028 |
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".