Evaluation of the Quality of Occupational Therapy Fieldwork Experiences
Bibliographic record
Abstract
Practice education, or fieldwork as it is referred to in occupational therapy, is a fundamental feature of occupational therapy education, priming students to become competent entry-level practitioners. Factors reported as contributing to poor quality fieldwork experiences include: students not receiving enough feedback; lack of opportunity to develop skills; and not being made to feel welcome in the environment. These are significant contributors to the overall development of competence so it is important to understand the current context of fieldwork experiences being offered in relation to the notion of quality in those learning environments. The purpose of this study was to evaluate the quality of the fieldwork learning environment from the perspective of occupational therapists’ working in one region of Canada. A validated survey, the Quality of Occupational Therapy Fieldwork Experience (QOTFE) tool, was used to identify features of what might constitute quality fieldwork education, and to determine whether there was a difference in quality of fieldwork experience between practice settings or types of practice areas. However, there was minimal variability in scores based on practice setting and practice area variables. These findings indicate a consistent quality of fieldwork experience across practice settings and practice areas. This research presents a picture of the current quality of fieldwork experiences available to occupational therapy students. This may be a starting place for further investigation into the factors that contribute to the quality of practice education learning experiences
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".