Changing Settlement Patterns Into Rural Built Environments: Impacts on Social and Environmental Determinants of Health For Immigrants in Region of Peel, Ontario
Bibliographic record
Abstract
This study examined changing settlement patterns into rural built environments and impacts on social and environmental determinants of health for immigrants in the rural town of Caledon, Ontario. Data was collected through focus groups and in-depth interviews with immigrant residents in addition to key informant interviews with service providers, and those with expertise in rural planning and/or immigrant settlement. Audie recordings were transcribed and thematically analyzed using NViso 12. This study is one of the first to integrate healthy built environments frameworks with social determinants of health frameworks and findings indicate that food system infrastructure; housing and rental stock; inclusive greenspaces are all factors that are important to the health and well being of immigrants in Caledon. The major challenge faced in terms of built form is inadequate public transit, which could have impacts on their mental and physical health. Further this study flags the importance of culturally appropriate religious and spiritual built amenities and services, something that is overlooked in healthy built environment research, underscoring the importance of an equity, diversity and inclusion lens. Various policy recommendations are provided that have the potential to enhance health and well-being of newcomers in the rural environments in Canada.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".