Optimizing staff and volunteer training at community-based exercise programs for persons with disabilities across Canada: A content analysis of quality participation being fostered in program training
Bibliographic record
Abstract
Community-based exercise programs (CBEPs) are promising avenues for enhancing exercise participation among persons with disabilities. The positive subjective experience of exercise participation (ie., quality participation (QP)) is important for sustained participation and enhancement to quality of life. Strategies for bolstering QP in CBEPs have been explored in previous research, but little is known about how program providers (i.e., staff, students, and volunteers) are being trained to foster experiential elements of QP (i.e., autonomy, belongingness, challenge, engagement, mastery, and meaning) for participants. The purpose of this study was to conduct an environmental scan of CBEP training materials for program providers to analyze for elements and strategies of QP that are being fostered. From a community of practice of Canadian CBEPs, 10 CBEPs provided training materials and additional information about training protocols through an online survey. Training materials were analyzed for elements and strategies of QP using dual deductive coding with an existing QP framework and strategy matrix. Frequency counts were calculated. Mastery strategies were coded most frequently across training materials and engagement strategies were least frequently coded. In a single program with unique training materials for all three provider types, belongingness, challenge, and mastery were consistently emphasized. Program materials contained a mean of 4.25/6 elements of QP, only one program covered all six elements across their training. Findings provide insight into how CBEPs currently integrate QP into program training, and this knowledge may contribute to establishing a national gold-standard training curriculum that emphasizes QP in CBEPs for persons with 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".