Enhancing Intraprofessional Collaboration in Occupational Therapy: Evaluation of an Online Course for Canadian OT and OTA Students
Bibliographic record
Abstract
An environmental scan conducted in 2022 highlighted a need for enhanced preparation for collaboration between occupational therapists (OTs) and occupational therapist assistants (OTAs) in Ontario, Canada. In response, an online course was developed to disseminate knowledge needed for effective OT-OTA intraprofessional collaboration (IntraPC) in the Canadian context. The objective of this study was to evaluate the effectiveness of the online course to improve perceived preparedness among Canadian OT and OTA students for OT-OTA IntraPC. Participants were Master’s-level OT students and diploma-level OTA students studying in Canada (N = 136), who were invited to complete the course by their program instructors. The course incorporated an integrated consent procedure and a retrospective pre/post-test survey, including the Interprofessional Collaborative Competencies Attainment Survey (ICCAS), to assess changes in participants' self-perceived collaborative competence following course completion. Results indicated a significant increase in self-perceived knowledge in and competence for OT-OTA IntraPC after completing the online course. The findings suggest that the online course effectively supports OT-OTA IntraPC preparation in Canada, marking a critical step toward enhancing collaborative practices in occupational therapy. While the course was effective, an online course alone is insufficient and additional scaffolded opportunities are needed to support OT-OTA IntraPC preparation and competence in real-world 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".