An Exploratory Study of the Occupation-Focused Models Included in Occupational Therapy Professional Education Programmes
Bibliographic record
Abstract
Introduction: Occupation-focused models are one method of integrating occupation into occupational therapy education programmes. They offer a theoretical basis for practice by providing explanations about the process and practice of the profession. Method: This exploratory study used a quantitative research framework to investigate which occupation-focused models are included in occupational therapy programmes, the rationale for their inclusion and how the models are integrated into curricula. A survey was sent to 193 occupational therapy programmes in Australia, Canada, the United Kingdom and the United States. Results: The response rate was 33.8% (n = 65). The data showed that each programme included between 3 and 10 occupation-focused models. The Canadian Model of Occupational Performance and Engagement and the Model of Human Occupation were included in 98.5% of all curricula surveyed. Conclusion: The educators used a multilayered decision-making process to determine which occupation-focused models to include in a curriculum. The instructional methods varied, but most programmes used practice placement education as a teaching strategy. Further research is required into how curriculum design influences a practitioner's future use of occupation-focused models in practice. A better understanding is needed of how professional practice education affects students' values and attitudes towards the use of occupation-focused models.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".