Being “outdoors” in a new country: associations between immigrant characteristics, outdoor recreation activities, and settlement satisfaction in Canada
Bibliographic record
Abstract
Many immigrants in Canada experience rapid mental health deterioration as they integrate into their host country. Participation in outdoor recreation, and natural environments at large, have been suggested as a health-promoting activity that facilitates immigrants’ adaptation, fostering mental health and wellbeing. We used cross-sectional data from the Canadian General Social Survey 2016 (n = 15,876) to explore the associations between immigrant characteristics (i.e. status, length of settlement, and migration programme), participation in outdoor recreation activities, and settlement satisfaction (operationalised as satisfaction with life in Canada and with the local environment). Our findings suggest that immigrants engage in significantly fewer outdoor activities, and settlement satisfaction varies according to the length of settlement and immigration programmes (i.e. refugees, family reunification and economic immigrants). Participation in outdoor recreation activities was associated with significantly higher levels of settlement satisfaction. Participation in a broader range of outdoor activities moderated the association between immigrant characteristics and satisfaction with the local environment. Our findings have implications for recreation professionals and settlement agencies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".