Exploring simulation design for mental health practice preparation: a pilot study with learners and preceptors
Bibliographic record
Abstract
The purpose of this exploratory pilot study was to determine the feasibility of delivering mental health practice simulations for occupational therapy learners, and whether different debriefing approaches yielded performance differences over successive simulations. Five clinical preceptors and nine first year MScOT students participated in this mixed-method study. In week one simulations, one student group received preceptor facilitated debriefing while the other group used self-debriefing. Both groups used the same scripted questions informed by an advocacy-inquiry approach. In the second week, both groups received the preceptor-led debriefing. Preceptors rated student performances while students self-rated their confidence, competence screen, and satisfaction using standardized tools. Ratings of simulation performance revealed gaps in practice knowledge and the process of practice. Preceptor-led debriefing for both groups resulted in greater depth of reflection and insight into learning gaps and opportunities for continued improvement. The self-debriefing group reported feeling less confident in the simulations. Preceptors reported the scripted advocacy inquiry debriefing approach helped draw out clinical reasoning that could not be observed from performance alone. Simulation may be an effective teaching tool for developing core practice competencies. Design and debriefing styles appear to impact preceptor feedback and the depth of learner critical reflection. Further study is required for generalization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".