Evaluation of the effects of health impact assessment practice at the local level in Monteregie
Bibliographic record
Abstract
BACKGROUND: In Quebec (Canada), the Monteregie Regional Public Health Department has chosen to use health impact assessment (HIA) to support municipalities through a knowledge exchange and collaborative process in order to positively influence decision-making regarding local policies and projects. The value of HIA is becoming increasingly recognized by municipalities interested in planning and managing their cities with an eco-systemic perspective. However, the knowledge and tools which support the use of the HIA at regional and local levels are still missing. METHODS: The general objective is to evaluate the impact the collaborative HIA process used in Monteregie has had on the formulation, adoption and implementation of policies and projects favourable to health. The methodology is based on Mayne's CA design, which allows the identification of factors which contribute to a change process. It is described as one of the best approaches to reduce uncertainty regarding the observed results and the contribution of a program. All of the HIA processes realised between January 2013 and January 2016 in Monteregie will be studied following a case study strategy. Study populations include regional and local public health professionals, municipal officers and community members implicated in these HIAs. Various qualitative and quantitative methods will be used, including examination of documentation, observations on the city grounds, and individual or group interviews. A model of change will be constructed for each HIA process and will present the logical pathway which leads to the observed results, alternative explanations and hypothesises as to why these results were obtained, and contextual factors that could have influenced them. This model will allow the production of a refined contribution story for each HIA. A convergence and divergence analysis will be completed in order to identify differences or similitudes between the different HIAs studied. DISCUSSION: In addition to contributing to the production of knowledge in relation to the collaborative model of HIA, this research project will allow other regional and local public health actors and municipalities of Quebec or other decision-making and political bodies to understand the usefulness of this approach for the improvement of population health and well-being.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.021 | 0.001 |
| 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 | 0.001 |
| 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 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".