Using simulation to explore medical students’ understanding of integrated care within geriatrics
Bibliographic record
Abstract
BACKGROUND: Given the increasing evidence and expansion of integrated care (IC) in healthcare, new IC curricula introduced early in undergraduate medical education (UME) are needed. Building on a pilot IC simulation called "Getting to Know Patients' System of Care" (GPS-Care), we aimed to explore students' understanding of patients' complex physical and mental health needs, and to increase our understanding of how students learned in this simulation. METHODS: 177 of 259 first-year medical students participated in GPS-Care at the University of Toronto. Students role-played an elderly patient or caregiver within 5 simulated healthcare professional appointments. Students completed written reflections and 7 students participated in one-on-one interviews. A thematic analysis of the reflections and transcripts was conducted and descriptive data was generated for questionnaires. RESULTS: Data saturation was reached at 43 reflections and 7 transcripts and the following themes emerged: a) students reflected on patients' complex care experiences, b) students reflected on of the healthcare system needs care, c) students increased understanding of IC, and d) students desire to improve the care of IC patients within the healthcare system. CONCLUSIONS: In addition to confirming previous pilot study themes, the results from this study identified the role of productive struggle to provide students with a deeper understanding of patients' IC care needs. Moreover, GPS-Care resulted in a transformative learning experience resulting in new insights into the importance of IC early in UME training.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".