The use of coaching in stroke rehabilitation: a scoping review
Bibliographic record
Abstract
PURPOSE: Stroke results in long-term impacts on a person's life requiring ongoing management after formal rehabilitation ends. Coaching can support people to build competencies and skills for managing health-related challenges and has the potential to support stroke survivors to continue achieving goals on their own following rehabilitation. This review sought to describe the research on coaching interventions for adults living with stroke. MATERIALS AND METHODS: A scoping review to explore how coaching is defined and used in stroke rehabilitation intervention research. PubMed, CINAHL, Medline, and PsycINFO databases were searched using terms to represent coaching, rehabilitation practitioners, and stroke, the results were extracted into COVIDENCE. Data were described and synthesized to identify similarities and differences among coaching interventions. RESULTS: Twenty-eight articles describing 15 interventions were included and categorized based on their focus as Health Coaching, Coaching for Exercise and Physical Activity, Coaching for Engagement in Activity or Participation, and Transition Coaching. Common elements of coaching interventions were goal setting, problem solving and education with emotional support being infrequent. Notably, coaching definitions and techniques were frequently not described. CONCLUSIONS: Coaching in stroke rehabilitation is diverse but has common foci and elements. More research using clear descriptions of coaching is required.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".