Decline of Empathy after the First Internship: Towards a More Functional Empathy?
Bibliographic record
Abstract
Research has shown a decline in empathy as medical studies progress. Among various hypotheses, an explanation track evoked is the first contact with the internship. Objectives This quasi-experimental study was designed to examine the impact of the first internship in medical students. Our research question was: "to what extent the first internship may decreased the empathy's scores of our 3d year medical students?" Methods We measured the empathy of 220 third year medical students before and after their first internship (3 weeks) in family medicine. Using online surveys methodology, we collected data about empathy ("Interpersonal Reactivity Index": IRI), epidemiology, professional orientation choices. Results Statistical analyses revealed a small but significant decrease in IRI's "fantasy," "empathic concern" and "personal distress" subscales. Conclusion These results suggest a potential impact of the first internship on empathic skills. The fact that the students' score for the "personal distress" subscale (which characterizes a difficulty in managing their emotions) decreases is actually a rather good thing. These data raise the question of the "function" of this loss of empathy. The fact that this score decreases after first internship, may indicate a positive change for these medical students: towards better emotional regulation and more functional affective 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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".