Stroke self-management and the role of self-efficacy
Bibliographic record
Abstract
PURPOSE: This study explored the impact of a 12-week stroke self-management program on occupational performance, the role of self-efficacy on improving occupational performance and the potential barriers and facilitators to self-efficacy as reported by clients and careers. MATERIALS AND METHODS: = 40) were recruited to participate in a self-management program after admission to hospital with diagnosis of stroke. A pre-post study was conducted and data were obtained from participants using: the Canadian Occupational Performance Measure, Stroke Self-efficacy Questionnaire, and semi-structured interviews with five participants and two careers. Data analysis was conducted using parametric statistics and thematic analysis. RESULTS: = 0.02). Three key barriers and facilitators emerged from the thematic analysis: "Support in making the transition home," "Getting back to normal," and "Reflecting on shared experiences." CONCLUSIONS: Engagement in a stroke-specific self-management program can improve client-perceived occupational performance and satisfaction. Self-efficacy was shown to be a mediating variable to occupational performance improvements. Future research should explore further the facilitatory strategies of self-efficacy during stroke self-management programs to maximize rehabilitation outcomes.Implications for rehabilitationMulti-modal self-management programs are recommended as effective for improving client-perceived occupational performance of people who have experienced stroke.Returning to valued occupations, goal setting, shared experiences, and local support are recommended components of a self-management program for stroke survivors.Focusing on enhancing client confidence, competence, and self-efficacy is recommended to achieve occupational performance gains through self-management.Occupational therapy coaching is recommended to guide participants through the self-management processes of goal-setting, shared problem-solving, performance evaluation, and reflection.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".