Analyzing the effects of health impact assessment on the Vieux-Sorel renewal project
Bibliographic record
Abstract
Background Health impact assessment (HIA) supports decision-making process which allows minimizing the negative impacts and maximizing the positive impacts on the health of the population. In this context, the Monteregie Directorate of Public Health set the initiative to accompany through HIA municipal actions, the municipalities of its region. We use a case of HIA which assessed the impacts of urban renewal on the built environment. We likewise aim to analyze the effects of HIA on decision-making and housing access, urban parks and greenspaces availability and active travel (walking and cycling) features that should support favorable health outcomes? Methods We applied contribution analysis which is a theory-based evaluation. Data gathering included semi-structured interviews with multiple stakeholders (N = 18), related documents and images of the modifications stemming from HIA on the project as well as data emanating from Canada censuses (2001, 2006, and 2011). Results The Vieux-Sorel is an impoverished neighborhood. For instance, the proportion of population living under the poverty line threshold was 29.8% whereas it was 11.3% in Sorel-Tracy city, 10.2% for the regional municipality of Pierre-De-Saurel, 8.7% for Montérégie health region and 12.2% in Quebec (Statistics Canada, 2013). HIA acts in synergy with contextual factors in local government decision-making. It generated little impact on housing while it helped to adopt measures on healthy parks and greenspaces. It also fostered the implementation of a healthy built environment through wide sidewalks, slight narrowing of street, bike lanes and street connectivity. Conclusions Our results suggest that HIA reinforces collaboration between public health and local government across a range of activities. It creates active travel features that could promote physical activity and has the potential to further the development of parks and greenspaces, thereby improving urban health. Key messages: It is essential to showcase the benefits of health impact assessment at municipal level, especially how it works, for whom and in what circumstances. Health impact assessment should be considered a priority when regenerating devitalized neighborhood aiming to enhance healthy built environment, thereby fostering resilient and healthy communities.
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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 | 0.036 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 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".