Raising awareness about physical activity’s role in reducing cancer risk: qualitative interviews with immigrant women and community agency managers
Bibliographic record
Abstract
OBJECTIVES: To explore how to reach immigrant women through community agencies to raise awareness of the role of physical activity (PA) in reducing cancer risk. STUDY DESIGN: Qualitative description. METHODS: We conducted semi-structured telephone interviews with immigrant women and community agency managers to discuss the ideal design of an education session on PA and cancer risk, and identified themes using content analysis. RESULTS: Participants included 22 women (6 African or Caribbean Black, 4 Chinese, 3 Filipino, 5 Indian, 4 Pakistani) and 16 agency managers from across Canada. Women were not familiar with Canada's PA guidelines, and few were aware that PA reduces the risk of cancer. All expressed interest in education about PA and cancer. Diverse women and managers expressed similar preferences for education session design including content (e.g. PA amount/type), format (e.g. in person preferred but virtual more practical), personnel (external expert plus agency staff), cultural tailoring (e.g. translated supplemental take home information) and reinforcing (e.g. follow-up with participants) strategies. Women and managers identified few barriers to participating in education sessions, chiefly, that women lacked time due to work and family responsibilities; and noted several enablers of participation (e.g. emphasize social aspect, provide gift cards or recreation centre passes). CONCLUSIONS: We generated insight on the ideal characteristics of a community-based education session that could raise awareness among immigrant women of the importance of PA in reducing cancer risk. Further research is needed to assess the feasibility and impact of PA education sessions designed based on these findings.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".