Factors Influencing Reflection and Self-assessment of Simulation Performance: Comparing Student and Preceptor Ratings
Bibliographic record
Abstract
Simulation with simulated patients is increasingly used in occupational therapy. Ensuring the debrief component meets student needs to develop their self-assessment skills for participating in a self-regulating profession is essential. This explanatory mixed-methods research sought to explore factors contributing to accurate and inaccurate self-assessment of simulation performance for novice occupational therapy learners in a part-time introductory fieldwork course. Self-ratings and preceptor-ratings of performance on eleven simulation objectives were compared for sixty-five novice occupational therapy students. Factor analysis was used to explore contributors to differences in ratings between students and preceptors. Students’ written plus-delta debrief reflections were analyzed to explore what evidence they used to self-assess performance and their remaining questions not addressed with self-debrief. There was a significant difference in the rating scores between students and preceptors for all objectives (p<.05). Students rated themselves on average higher than preceptors and they often missed safety concerns noted by the preceptors. Factor analysis indicated that the type of learning objective contributed to rating difference with objectives related to communication differing from those related to demonstration of skills. Deductive qualitative content analysis of reflections indicated that students give significant weight to simulated patients’ agreeability, willingness to participate, and reported comfort as evidence of success when reflecting on simulations, and rarely use best-practice guidelines, theories, or principles to self-assess their performance. Novice students may need guidance and explicit training on what diverse types of evidence they may use to support self-assessment and reflection on performance in a simulation for different types of learning objectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".