Enablers and Barriers in Delivery of a Cancer Exercise Program: The Canadian Experience
Bibliographic record
Abstract
BACKGROUND: Exercise is an important therapy to improve well-being after a cancer diagnosis. Accordingly, cancer-exercise programs have been developed to enhance clinical care; however, few programs exist in Canada. Expansion of cancer-exercise programming depends on an understanding of the process of program implementation, as well as enablers and barriers to program success. Gaining knowledge from current professionals in cancer-exercise programs could serve to facilitate the necessary understanding. METHODS: Key personnel from Canadian cancer-exercise programs (n = 14) participated in semistructured interviews about program development and delivery. RESULTS: Content analysis revealed 13 categories and 15 subcategories, which were grouped by three organizing domains: Program Implementation, Program Enablers, and Program Barriers. ■ Program Implementation (5 categories, 8 subcategories) included Program Initiation (clinical care extension, research project expansion, program champion), Funding, Participant Intake (avenues of awareness, health and safety assessment), Active Programming (monitoring patient exercise progress, health care practitioner involvement, program composition), and Discharge and Follow-up Plan.■ Program Enablers (4 categories, 4 subcategories) included Patient Participation (personalized care, supportive network, personal control, awareness of benefits), Partnerships, Advocacy and Support, and Program Characteristics.■ Program Barriers (4 categories, 3 subcategories) included Lack of Funding, Lack of Physician Support, Deterrents to Participation (fear and shame, program location, competing interests), and Disease Progression and Treatment. CONCLUSIONS: Interview results provided insight into the development and delivery of cancer-exercise programs in Canada and could be used to guide future program development and expansion in Canada.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".