Using Occupational Therapy Assistant Perspectives to Teach Occupational Therapy Supervisory Roles and Expectations
Bibliographic record
Abstract
Occupational therapy assistant (OTA) supervision is an expected skill and role of entry-level occupational therapists (OTRs). The purpose of this convergent mixed-methods study is to provide occupational therapy students (OTSs) with an interactive and collaborative educational opportunity, using an OTA-perspective panel discussion to improve the learning of effective supervision and role delineation. Participants consisted of OTSs (n = 11) in a graduate master’s program and a panel of OTAs (n = 10). All participants were provided with a standard lecture on the topic of supervision, followed by a pre-test survey. Then, they participated in a guided panel discussion followed by a post-test survey. Results suggest that an OTA-perspective panel discussion can enhance the learning of supervisory roles and expectations to OTSs, beyond what was provided in the standard lecture (p = 0.007). Further data was gathered of all participants consisting of qualitative perspectives. Thematic analysis resulted in enhanced learning of role-delineation, professional perspectives, and supervisory experiences. The results of this study suggest that occupational therapy programs would benefit from similar OTA-led perspective discussions to enhance OTSs understanding of skills needed to be effective supervisors as entry-level occupational therapists.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".