Patient and professional perspectives on physical activity promotion in routine cancer care: a qualitative study
Bibliographic record
Abstract
BACKGROUNDS: Physical activity is associated with many benefits in reducing cancer symptoms and treatments side effects. Yet, studies consistently show that knowledge about physical activity is under-promoted among people diagnosed with cancer. Therefore, we aimed to contribute to filling this gap by ascertaining patient and professional perspectives regarding physical activity promotion. METHODS: This study took place in Montreal, Canada. We conducted individual, semi-structured interviews with cancer patients who participated in a physical activity program and professionals working in the healthcare system. Participants had to be aged over 18 years, be able to communicate verbally in either English or French, and consent to an audio-recorded interview. A hybrid deductive-inductive approach to content analysis was applied to analyze interview transcripts using Dedoose and Microsoft Excel software. RESULTS: Our sample comprised 21 patients (76.2% women) and 20 professionals (80% women). We identified 24 factors (barriers, facilitators, and improvement suggestions) influencing physical activity promotion across organizational, community, and social levels. Results suggest that to improve physical activity promotion in cancer care, it is necessary to showcase exercise specialists as a healthcare resource, to champion for this change within health organizations, to develop partnerships between public and private sectors of the health and fitness industries, and to reassess social norms concerning cancer survivorship and treatment. CONCLUSION: These findings shed light on the gaps and the bright lights in physical activity promotion for people diagnosed with cancer across numerous levels.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".