Sequential Simulations During Introductory Part-Time Fieldwork: Design, Implementation, and Student Satisfaction
Bibliographic record
Abstract
Background: Simulation is used in various ways in occupational therapy education and is recognized as a replacement for some conventional fieldwork hours. However, design and student satisfaction has had limited exploration. Method: Sequential best practice simulations were designed for Level 1 fieldwork objectives in mental and musculoskeletal practice. The Satisfaction with Simulation Education scale (SSES) and qualitative feedback were used to assess student satisfaction. An exploratory factor analysis was used to validate the SSES in occupational therapy, and a three-factor repeated measures ANOVA was used to determine factors contributing to satisfaction across simulations. Results: A three-factor model of clinical reasoning and ability, facilitator feedback, and reflection was derived. The qualitative data identified authenticity and relevance to clinical practice as two domains not captured by the SSES items. Repeated measures ANOVA revealed a significant interaction of case by SSES factor with mental health clinical reasoning and ability mean scores lower than musculoskeletal means. Conclusion: Occupational therapy students reported high levels of satisfaction for design used to prepare for full-time fieldwork experiences. The SSES captured most contributors to satisfaction, but potential items to enhance the SSES validity in occupational therapy include those related to authenticity and relevance to 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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".