Teaching Critical Reflection in Occupational Therapy: A Quantitative Pre/Post Evaluation of Student Learning
Bibliographic record
Abstract
This study was designed to answer the question, “Does student recognition of the assumptions underlying occupational therapy practice increase after participation in guided exercises of critical reflection?” The project is grounded in critical occupational therapy (as first named by Whiteford and Townsend) to promote students to reflect actively on dominant disciplinary ideologies and self-reflect on how one’s social positionality impacts understandings of occupational participation. The concept of non-sanctioned occupations was drawn on as a means to facilitate changes in student recognition. A one group, quantitative pre/post design with six open-ended responses was undertaken with master’s-level students (N = 53) at two private universities. A learning module involving an interactive 90-min lecture with a pre-assigned reading and associated reflection guide was used to facilitate questioning of disciplinary ideologies. Even when introduced in small amounts, the use of critical occupational therapy in education shows benefits. Wilcoxon signed rank tests indicated the learning model increased student recognition of underlying personal assumptions. Thematic qualitative analysis confirmed these results and described patterns of change between pre/post surveys. This study demonstrates the potential for occupational therapy curricula to embed methods that instill critical self-reflection in emerging practitioners, with the promise of transforming clinical assessment and 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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".