Evaluating Behaviour Change Training Modules for Kinesiologists: Enhancing Adapted Physical Activity Service Provision for Two Community Organizations
Bibliographic record
Abstract
Background. People with disabilities can benefit from community-based physical activity programs. However, there is a lack of evidence-based tools to support kinesiologists’ training within these adapted physical activity programs, such as Adaptavie and Viomax. The purpose of this study was to co-create and evaluate the implementation of physical activity training modules to meet the needs of Adaptavie and Viomax for improved staff training. Methods. A pre-post quasi-experimental design was conducted to evaluate the changes in capability, opportunity, and motivation (COM-B) of kinesiologists (N=14) after completing nine training modules on motivational interviewing, five self-enactable behaviour change techniques and adapted physical activity prescription. Participants' ability to apply the concepts from the modules and the quality of skills used were assessed using the MITI 4.2.1 coding tool during testing on a standardized mock client. Participants also rated the feasibility of each module. Results. Medium to large effects were observed in participants' capability (Hedge’s g= 0.67-1.19) across 8/9 modules, opportunity (Hedge’s g= 0.77-1.38) across all modules, and motivation (Hedge’s g= 0.58-1.03) across 6/9 modules. All 9 modules were rated as feasible. When opportunities presented themselves, 77.78% to 100 % of participants used the five BCTs in the mock client session. All participants had successful attempts to apply the action planning and problem solving BCTs. Participants demonstrated good use of technical and relational motivational interviewing strategies, complex reflections, and favored adherent motivational interviewing language over non-adherent language. Conclusion. Training kinesiologists is feasible and effective, with potential to enhance adapted physical activity programs for individuals 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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".