Physiotherapists supporting self-management through health coaching: a mixed methods program evaluation
Bibliographic record
Abstract
PURPOSE: To evaluate a program in support of chronic disease self-management (CDSM) that is founded on a health coaching (HC) approach, includes supervised exercise and mindfulness-based stress reduction components and is delivered within a private practice physiotherapy setting. METHODS: An explanatory mixed method design, framed by theory-based program evaluation, was employed to evaluate an eight-week group-based program. Standardized self-rated and performance measures were evaluated pre- and post intervention. Additionally, participant focus groups were conducted following the intervention period. An inductive thematic approach was undertaken to analyze the qualitative data. FINDINGS: Seventeen participants (N = 17) completed the study. Improvements were seen in both self-report and performance outcomes. Participants explained how and why they felt the program was beneficial. Six themes were generated: (1) group dynamic; (2) learning versus doing; (3) holism and comprehensive care; (4) self-efficacy and empowerment; (5) previous solutions versus new management strategies; and (6) healthcare provider support. CONCLUSIONS: This study established that a group program in support of CDSM founded on a HC approach demonstrated potential value from participants as well as favorable outcomes. A pragmatic randomized control trial is required to determine efficacy of this intervention.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".