Barriers and Facilitators to Initial and Ongoing Implementation of Community-Based Exercise Programs for Persons with Physical Disabilities: Qualitative Perspectives of Program Providers
Bibliographic record
Abstract
Community-based exercise programs (CBEPs) designed for persons with physical disabilities can promote participation in physical activity (PA). Despite their importance, few CBEPs for persons with physical disabilities exist in Canada. Understanding successful CBEP implementation may provide exercise providers with a framework to support the development, implementation, and long-term sustainability of CBEPs. The purpose of this study was to explore CBEP providers’ perceptions of the barriers and facilitators surrounding the initial and ongoing implementation of CBEPs using the Consolidated Framework for Implementation Research 2.0 (CFIR 2.0). Fifteen eligible CBEPs were identified, of which nine program providers expressed interest in participating in semi-structured interviews. Transcripts were subject to inductive thematic analysis, and codes were deductively mapped onto domains of the CFIR 2.0. Barriers and facilitators were organized into six overarching themes and eighteen subthemes. Across themes, barriers and facilitators were present through initial and ongoing implementation and spanned all five domains of the CFIR 2.0, suggesting factors at all levels influence CBEP implementation. Ultimately, the barriers and facilitators to CBEP implementation may act as a roadmap to support the creation and sustainability of new and existing CBEPs, thereby increasing the number of programs that offer PA opportunities for persons with physical disabilities.
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".