Occupational Therapy Students’ Perceptions of Feedback During Pre-Fieldwork Simulation Debrief: Useful and Why
Bibliographic record
Abstract
Simulation is increasingly used in occupational therapy education with the objectives of developing practice skill competency and enhancing clinical reasoning. Debriefing, an integral part of the simulation process, is critical to achieving these objectives. This study sought to determine the types of debrief feedback Master of Science in Occupational Therapy (MScOT) students perceived as most useful and why, and how the advocacy inquiry model of debriefing influenced self-reported increases in clinical reasoning, client care, and planned implementation of feedback in practice. Using an embedded mixed method design with secondary data analysis, sixty-three first-year MScOT students provided 357 descriptions of the most useful feedback they received during 10-minute, facilitator-led debrief sessions after six simulations. Qualitative analysis revealed useful feedback was related to specific skills, interviewing and communication, the process of practice, strengths and encouragement, and client-centeredness. The advocacy inquiry approach was a useful delivery method of feedback. Logistic regression indicated that reported use of the advocacy inquiry model increased the likelihood by 4.7 times that students reported the debrief facilitated clinical reasoning. When advocacy inquiry was used in conjunction with providing feedback on specific skills, students were 5.3 times more likely to report planned implementation of the feedback in practice. Students value a variety of types of feedback during simulation debriefs. Debriefs using the advocacy inquiry method may be particularly useful for facilitating the development of clinical reasoning in the context of simulation-based fieldwork education.
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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 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".