Exploring preventable lifestyle risk factors among newcomers in Montreal, Canada: a mixed-method study
Bibliographic record
Abstract
Newcomers in Montreal, Quebec-including immigrants, international students, refugees, and asylum seekers-face lifestyle risk factors associated with chronic conditions. Gaps in the literature highlight methodological limitations in the previous studies as well as incomplete examination of physical activity, diet, smoking, alcohol use, and stress in this population. This study aims to examine these preventable lifestyle risk factors among Montreal newcomers and explore barriers and facilitators influencing them. Using a community-based participatory approach, we conducted a concurrent mixed-methods study, collecting data via surveys and focus groups. Survey data were analysed descriptively, and focus groups underwent thematic analysis. Among 149 survey and 55 focus group participants (equal gender distribution, mostly aged 18-29), engagement in physical activity varied. Barriers included weather, health issues, cultural adjustments, and lack of motivation; facilitators included social support and health concerns. Dietary habits favoured home-cooked meals, but significant fast-food consumption occurred due to time, cost, access, food quality challenges, dietary preferences, and nutritional awareness were facilitators. Smoking rates were low; many abstained from alcohol, with social influences as barriers and family support as facilitators. Stress levels were moderately high due to various pressures: coping strategies included therapy, physical activity, social support, and a positive mindset. Newcomers in Montreal display both healthy and risky lifestyle behaviours, with concerns around diet and stress. Targeted health promotion strategies addressing specific barriers and leveraging facilitators are needed to improve their health and well-being.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".