Providing Remote Students with Access to a Video-enabled Standardized Patient Simulation on Interprofessional Competencies and Late-life Depression Screening
Bibliographic record
Abstract
Background Standardized patient (SP) simulation is used to teach geropsychiatry. This project tested feasibility and effectiveness of video-enabled SP simulation to teach interprofessional (IP) late-life depression screening.Methods and findings Nurse practitioner, pharmacy, and medical students (N=177) participated in remote (n = 27) and on-site (n = 150) SP simulation. Linear mixed-effect model determined the effects of time and setting on pretest and posttest Interprofessional Education Collaborative Competencies Attainment Survey (ICCAS) data. Overall, no significant difference was observed in degree of change on ICCAS domains, indicating both modalities produced equally beneficial outcomes. Small sample size and focus on late-life depression screening limits generalizing results.Conclusions Video-enabled SP simulations can be incorporated to prepare students with IP competencies for late-life depression screening.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".