Impact of a longitudinal course on medical professionalism on the empathy of medical students
Bibliographic record
Abstract
OBJECTIVE: Medical education should enhance empathy. We examined, using self-assessment instruments and standardized patients (SPs), the impact on empathy, of a multi-year intervention (years 4-6 of medical training) that uses reflective learning approaches. METHODS: 241 final-year medical students participated; 110 from the 2018 graduation class (non-intervention group) and 131 from the 2019 graduation class (intervention group). Participants completed two self-reported empathy questionnaires - the Jefferson Scale of Empathy-Students (JSE-S) and the Interpersonal Reactivity Index (IRI) - and a personality questionnaire, the NEO Five-Factor Inventory. Additionally, SPs in a simulated station assessed participants' empathy with two patient-reported instruments: the Consultation and Relational Empathy (CARE) scale and the Jefferson Scale of Patient Perceptions of Physician Empathy (JSPPPE). RESULTS: Empathy scores were significantly higher in the intervention group compared to the non-intervention group when assessed by the SP (p < 0.001). No differences were found in self-reported questionnaires between the two groups. CONCLUSION: A longitudinal, multi-year reflection-based intervention enhanced empathy amongst medical students as assessed by SPs, but not when assessed by student self-reported measures. PRACTICE IMPLICATIONS: Multi-year reflective learning interventions during clinical training nurture empathy in medical students. Assessments completed by SPs or patients may enhance the evaluation of empathy.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".