Examining the Effectiveness of Caregiver-Mediated CO-OP on Occupational Performance (Generalization and Transfer) and Self-efficacy in Patients With Chronic Stroke: Protocol of a Double-Blind Randomized Control Trial Study
Bibliographic record
Abstract
Background and Aims Patients with brain stroke often face a combination of post-stroke impairments that can limit activity and participation. Various treatment approaches are used in brain stroke rehabilitation by occupational therapists. Still, these approaches have poor evidence for improving generalization and transfer of occupational skills and maintaining the improvement achieved in the long term. Therefore, in recent years, using the cognitive orientation to daily occupational performance (CO-OP) approach in the rehabilitation of people with stroke has received attention. In previous studies, caregiver participation, one of the key features of the CO-OP approach, has received less attention. Therefore, the present study aimed to investigate the effectiveness of the CO-OP approach based on caregiver training on occupational performance (generalization and transfer) and self-efficacy in people with chronic brain stroke. Methods This double-blind clinical trial will include 20 participants with chronic brain stroke. In this study, the effect of CO-OP based on caregiver training on occupational performance and self-efficacy of chronic stroke survivors will be compared with CO-OP implemented directly by an occupational therapist. A CO-OP-trained occupational therapist will provide the CO-OP intervention in both groups in 10 to 12 sessions twice weekly. In the experimental group, caregivers will be required to attend all sessions. The assessments will be conducted by an assessor unaware of the group allocation before the intervention, immediately after the intervention, and one month after the intervention. Patients will also be unaware of the grouping. Primary outcomes will be the Canadian occupational performance measure (COPM), performance quality rating scale (PQRS), and general self-efficacy scale (GSE-10) tests, and secondary outcomes will be assessed by assessing the quality of life of participating caregivers through the World Health Organization Quality of Life test. Conclusion It is expected that the results of this study provide useful information about the effect of applying the CO-OP approach based on caregiver training on occupational performance and self-efficacy in people with chronic stroke.
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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 | 0.036 | 0.029 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".