A qualitative analysis of motives, barriers and enablers to engaging in physical activity
Bibliographic record
Abstract
Physical activity (PA) rates are low among breast cancer survivors (BCS). In order to promote well-being in this growing population, understanding the enablers and barriers to engaging in PA may help inform the development of targeted interventions aimed at increasing PA levels in BCS. This qualitative study described women's motives, perceived barriers and facilitators to engaging in PA following the completion of primary treatment for breast cancer. Semi-structured interviews were conducted with a purposeful sample of 11 BCS (Mage = 56.7 years; Mtime since treatment = 4.45 years). Interviews were audio-recorded and transcribed verbatim. Data were analyzed using an inductive analysis approach. Motives to engage in PA included psychological/emotional well-being, appearance management, physical health, social relationships, and feeling pressured by others. Perceived barriers included time constraints, physical limitations, lack of motivation, competing responsibilities, lack of knowledge, lack of facilities/opportunities, weather conditions, and limited tangible support (e.g., direct involvement of friends/family). Facilitators included accessibility of facilities, informational support, and the presence of a PA partner. Overall, BCS were physically active for similar reasons as those reported by non-clinical samples of women. Researchers should focus on understanding how women's motivational needs may be met in order to effectively increase PA. Most barriers that constrained BCS' PA participation appear to be amenable to change. Thus, discussion of barriers to engaging in PA may be a key component of PA programs. Furthermore, promoting facilitators may also be an important part of PA programs for BCS.Acknowledgments: The first author is supported by a Canada Graduate Scholarship from the Social Sciences and Humanities Research Council of Canada (SSHRC) and a CIHR strategic training award.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".