Patients as teachers: Evaluating the experiences of volunteer inpatients during medical student clinical skills training
Bibliographic record
Abstract
PURPOSE: Early patient encounters in medical education are an important element of clinical skill development. This study explores the experiences of volunteer inpatients (VIPs) participating in clinical skills training with junior medical students (JMS) solely for educational purposes. METHODS: Following first-year medical students practicing history taking and clinical examinations with VIPs at Toronto General Hospital (TGH) and Toronto Western Hospital (TWH), patients completed a questionnaire and a short audio-recorded interview. This study used a mixed methodological approach. A 5-point Likert-scaled survey queried satisfaction regarding the recruitment process, student and faculty interactions and patient demographics (e.g. age and educational background). A 10-minute follow-up interview investigated patient perspectives. Survey responses were correlated to patient demographics and descriptive thematic analysis summarized trends in patient perspectives. RESULTS: Of 93 consenting VIPs, 66% were male and 58% participated at TGH. The mean overall experience was positive (4.76 and 4.93 at TGH and TWH, respectively). Three themes emerging through thematic analysis were Not "Just" a Medical Student, Patient as Teacher, and Promoting Best Practices. VIPs reported positive experiences when they were adequately informed of the VIP role during recruitment, and when students exhibited confidence, interest, and respect throughout the session. CONCLUSION: Study results provide clarity about VIP experiences with JMS and lay a foundation for improved patient satisfaction and best practices within clinical skills curricula in the health professions.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".