Evaluating a simulation-based interprofessional education activity on disaster preparedness and management among health professions students
Bibliographic record
Abstract
BACKGROUND: Simulation-based education offers a risk-free platform to prepare future health professionals for interprofessional collaboration during high-stakes emergencies. This study involved the design, implementation, and evaluation of a disaster-focused simulation to enhance interprofessional competencies among health professions students. METHODS: An interprofessional education (IPE) simulation covering the four disaster preparedness and management phases (mitigation, preparedness, response, recovery) was conducted for undergraduate health professions students. Students, assessors, and standardized patients (SPs) participated in the evaluation. Data on interprofessional competencies were collected from students using the Team's Perception of Collaborative Care Questionnaire, from assessors using the Modified McMaster-Ottawa Scale, and from SPs using the Standardized Patient Team Evaluation Instrument. Descriptive statistics were used to summarize study variables. Paired sample t-tests were conducted to compare score differences between assessors. Learning curve across cases were tested using one-way repeated measures ANOVA, and associations between global scores and demographic variables were analyzed using t-test or ANOVA, as appropriate. RESULTS: Thirty-three students, 13 assessors, and 8 SPs participated in the evaluation. response rates were 33.3% (students), 92.9% (assessors), and 100% (SPs). Students self-reported positive perceptions of teamwork in the activity, with over 90% agreement across all domains. Assessors' ratings for the response phase corroborated these findings, with over 80% of students scoring at or above expectations in all domains. SPs' evaluations were also high, with 70% agreeing that students demonstrated positive interprofessional practice behaviors. For the diabetic ketoacidosis case, teams' global performance scores were calculated as the mean of the two assessors' ratings. Students with prior IPE experience (M = 2.42, 95% CI: 2.24-2.60) and those who had completed a prior practice placement (M = 2.48, 95% CI: 2.30-2.65) performed significantly better than students without IPE experience (M = 2.06, 95% CI: 1.80-2.33) or a prior practice placement (M = 2.12, 95% CI: 1.86-2.37). While not statistically significant, a trend towards improved performance across cases in the response phase suggested a learning curve effect. CONCLUSIONS: Simulation-based IPE can strengthen interprofessional competencies for disaster preparedness and management, with greatest benefit when preceded by other IPE activities and clinical placements.
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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 | 0.010 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".