Problem-based learning in occupational therapy: why do health professionals choose to tutor?
Bibliographic record
Abstract
For over 20 years the occupational therapy programmes offered by McMaster University and Mohawk College, Hamilton, Ontario have used small-group, problem-based learning tutorials as a major component of their curriculum. These programmes were among the first occupational therapy programmes in the world to use a problem-based tutorial format. The inclusion as tutors of both full-time faculty and clinicians, from all clinical practice areas, was central to the design of the problem-based learning courses. A survey of all tutors from the last 20 years collected information about why health professionals are motivated to tutor and what they see as challenges to maintaining this educational role. Three primary themes emerged from the data: being an educator; being a learner and present and future challenges to continuing with the tutoring role. Within the educator theme there was a secondary theme of professional duty or obligation. In addition, the participants identified suggestions for enhanced support and continuing education for tutors. This article summarizes the findings of the survey.
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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 | 0.008 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".