Meeting the Challenge of Assessing Clinical Competence of Occupational Therapists within a Program Management Environment
Bibliographic record
Abstract
BACKGROUND: Program management models have raised concerns among occupational therapists about professional standards related to clinical competence, performance review procedures, and quality improvement initiatives. PURPOSE: This paper describes how a chart-stimulated recall (CSR) peer-review process and interview tool was revised, implemented, and evaluated as a pilot project to assess the clinical competence of occupational therapy staff at a large urban health centre in southern Ontario. METHODS: Fourteen pairs (n=28) of occupational therapists representing various practice areas participated in this project. Half served as peer assessors and half as interviewees. Peer assessors conducted an independent chart review followed by a one-hour personal interview with a peer partner to discuss clinical management issues related to the client cases. Each interviewer rated his or her partner's clinical competence in eight areas of performance using a 7-point Likert scale. FINDINGS: Results indicated that the CSR tool could discriminate among occupational therapists in terms of overall levels of clinical competence and also identify specific areas of concern that could be targeted for professional development. Feedback from participants was positive. CONCLUSIONS: The CSR tool was found to be useful for assessing clinical competence of occupational therapists in this large health centre as a quality improvement initiative within that discipline group. Further research is needed to establish the reliability and validity of the CSR tool.
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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 | 0.160 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".