Co-creation and Evaluation of an Adapted Physical Activity Toolkit: Guidelines To Support Practice Among Rehabilitation Professionals in Community Organizations
Bibliographic record
Abstract
Background: Limited knowledge and skills of health professionals is a common barrier when adapting physical activity for people with disabilities. A community organization (Adaptavie) identified the need for improved training and resources to facilitate APA prescription by their employees. The objectives of this research were to co-create an APA toolkit and evaluate implementation. Methods: A multi-method participatory research approach was used with kinesiologists who worked at Adaptavie. The project consisted of two phases: P1) co-creation of the toolkit; P2) implementation evaluation. Sociodemographic information (P1; P2), the Work self-efficacy Inventory Survey (P1; P2), the Indicators of Success Questionnaire (P2) and focus groups (P1 n = 3; P2 n = 1) were conducted with kinesiologists. Summary statistics were described (sociodemographic and questionnaires) and analysed thematically (focus groups). Results: The co-creation of an evidence-based training toolkit contained information about 45 types of disabilities. Five to eight kinesiologists (depending on the phase) reported improvements in workplace self-efficacy, skills and knowledge after using the APA toolkit for one year. Following implementation, the APA toolkit was reported to have a high level of usability and fidelity. Conclusion: A co-created APA toolkit supported kinesiologists to prescribe evidence-based APA programs by increasing their knowledge, skills and self-efficacy.
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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 | 0.165 | 0.204 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".