Evaluation of a survey to put health priorities on the local political agenda
Bibliographic record
Abstract
Introduction : Built environments are known health determinants and they play a major role in health inequalities. Using the protocol of LARES (Large Analysis and Review of European Housing and Health Status), a survey conducted by the World Health Organisation (WHO) in 2002-2003, we realized a pilot project in a small French-Canadian municipality (population:7000). We wanted to inform local policy-makers about priorities related to health and empower them in decision-making to promote the development of sustainable solutions. Methods: The survey targeted a sample of 200 households in two different areas of the municipality (area 1: less favored; area 2: more favored). Data were collected with four tools (three questionnaires verifying perception of housing, health status and housing expenses, and a standardized grid for visual inspection). We evaluated the project using five different research questions with key informants verifying: 1.who were involved in the project (actual vs projected); 2. if the method was applied as projected; 3. what was the satisfaction of the participants; 4. if the transfer of the results was useful; 5. what were the resources needed to realize the project. This abstract concerns mainly the questions 4 and 5. Results: Data were collected on 161 households (area 1: n=92; area 2: n= 69). Even with a lower participation rate than expected, the survey served as a catalyst to guide public health decisions on a local basis. The results of the survey were used: 1. to animate discussions concerning people living in bad sanitary conditions; 2. in final drafting of the urban plan and the development of parks, green spaces and bike paths; 3. to allow the town to meet criteria for access to renovation programs. Conclusions: The survey identified health priorities and permitted to set these priorities on the agenda of policy-makers. For this municipality, it was an important public health tool.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".