Use of Occupational Performance Coaching for stroke survivors (OPC-Stroke) in late rehabilitation: A descriptive case study
Bibliographic record
Abstract
Background: Stroke is a leading cause of disability in adults. Following stroke, 60% of people report needing help with everyday activities, and 80% report having very few meaningful activities. These restrictions often continue for years. This study explored the efficacy of Occupational Performance Coaching for stroke survivors (OPC-Stroke) on the participation level of adults in the later stage of stroke rehabilitation. Method: A descriptive case study design was used. One participant in the later stages of rehabilitation was recruited. Outcome measures for participation, goal performance and satisfaction, and emotional well-being were administered pre and postintervention to observe for direction of change. A semi-structured interview was carried out postintervention to explore the participant’s experiences of the intervention. Results: The participant who took part in the study reported improvement with his goal performance and satisfaction. However, the level of participation did not improve and emotional well-being decreased. Qualitative data revealed an appreciation of the intervention and a recommendation of the intervention for others. Conclusion: OPC-Stroke was valued by the participant and shows promise for improving goal performance and satisfaction. Further research is necessary to determine the potential efficacy of OPC-Stroke in later stages of rehabilitation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".