The use of coaching in occupational therapy: An integrative review
Bibliographic record
Abstract
BACKGROUND/AIM: Coaching has been identified as a core enablement skill of occupational therapists. Occupational therapists have begun to embrace the use of coaching as a therapeutic tool to promote client-centeredness in their practice. As the use of coaching becomes more popular it is important to examine and evaluate coaching use in occupational therapy practice to clarify what is meant by coaching and inform future research and practice in this area. METHODS: An integrative literature review was conducted to examine how coaching is being used by occupational therapists, identify the similarities and differences between coaching interventions and to identify the empirical evidence for the use of coaching in occupational therapy. RESULTS: The literature search resulted in 24 articles describing 11 different interventions that reported use of coaching methods by occupational therapists with various populations. Similarities among interventions included goal setting, problem solving and an educational component. Differences in the directiveness of the occupational therapist were evident. The level of research evidence for individual interventions ranges from low to moderate. CONCLUSIONS: Differences are evident in the coaching theories and methods used in occupational therapy. While evidence of effectiveness of these interventions is promising, study designs used to date are vulnerable to bias and have had small sample sizes, limiting the strength of evidence. More research using clear descriptions of the coaching approach and more robust research methods is needed to better inform clinical practice.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".