‘We just don’t have this in us…’: Understanding factors behind low levels of physical activity in South Asian immigrants in Metro-Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: South Asian immigrants in western countries are at a high risk for metabolic syndrome and associated chronic disease. While a physically active lifestyle is crucial in decreasing this risk, physical activity (PA) levels among this group remain low. The objectives of this study were to explore social and cultural factors that influence PA behavior, investigate how immigration process intersects with PA behaviors to influence PA levels and to engage community in a discussion about what can be done to increase PA in the South Asian community. METHODS: For this qualitative study, we conducted four Focus Group Discussions (FGDs) among a subset of participants who were part of a larger study. FGD data was coded and analysed using directed content analysis to identify key categories. RESULTS: Participants expressed a range of opinions, attitudes and beliefs about PA. Most believed they were sufficiently active. Women talked about restrictive social and cultural norms that discouraged uptake of exercise. Post-immigration levels of PA were low due to change in type of work and added responsibilities. CONCLUSION: Health promoters need to consider social, cultural, and structural contexts when exploring possible behavior change interventions for South Asian immigrants.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".