Exercise preferences, barriers, and facilitators of individuals with cancer undergoing chemotherapy prior to stem cell transplantation: A mixed- methods study
Bibliographic record
Abstract
Background: Exercise can help to mitigate side effects of hematopoietic stem cell transplantation (HSCT), particularly when initiated prior to HSCT. However, the exercise-related barriers, facilitators, and preferences of this patient population remain unclear. This study aimed to explore the patient experience in order to inform future implementation of a prehabilitation intervention. Methods: A sequential explanatory mixed methods study was conducted in two phases: (1) cross-sectional survey, and (2) focus groups. Survey questions were framed to align with the Theoretical Domains Framework. Focus group data were analyzed using a directed content analysis approach, followed by inductive thematic analysis to generate themes that represented the exercise-related barriers, facilitators, and preferences of participants. Results: Twenty-six participants completed phase one; 11 went on to complete phase two. 81% (n=21) of participants reported knowing the benefits of exercise pre-HSCT, while 50% (n=13) felt they knew how to exercise safely. Only 50% (n=13) were fairly/very confident in their ability to exercise pre-HSCT. Barriers to exercise included limitations in knowledge and skills, inadequate healthcare provider support/availability, and the emotional toll of treatment. Facilitators included social support from others, and goals. Participants’ exercise preferences were related to two themes: (1) Program Structure (sub-themes: prescription, scheduling, mode of delivery); (2) Support (sub-themes: support from personnel, tailoring, education). Conclusion: Findings suggest that exercise prehabilitation may be optimized by enhanced education, inclusion of an exercise professional in the pre-transplantation healthcare team, a virtual or hybrid delivery model, and flexible program scheduling.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".