Occupational Therapists’ Psychotherapy Competence: A Scoping Review of Secondary Data
Bibliographic record
Abstract
Background. Occupational therapists have been writing about and practicing psychotherapy for almost a century. However, questions about competence and tensions regarding psychotherapy in occupational therapy persist both within and outside the profession. Purpose. To explore the scope of the existing literature on psychotherapy competence written by occupational therapists and/or pertaining to occupational therapy research or practice. Method. A secondary analysis of the 207 articles included in the scoping review by Marshall and colleagues was conducted. Using inductive and deductive approaches, data from 207 articles were screened, extracted, and analyzed to identify themes related to competence in psychotherapy. Findings. The 104 articles included spanned from 1927 to 2020; 50% were non-empirical. The narrative synthesis had one overall theme, Professional Identity, and three subthemes: Competence, Attaining and Maintaining Competence, and The Great Debate. There was no consistent pathway outlined for occupational therapists to attain psychotherapy competence, which may contribute to role confusion and dissonance. Conclusion. This review revealed the reciprocal relationship between professional identity and psychotherapy competence in occupational therapists. Future research should explore how the use of psychotherapy competence pathways impacts professional identity and contributes to practice competence.
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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 | 0.042 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.057 | 0.043 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".