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Record W4417332189 · doi:10.1177/23821205251405320

Restoring Empathy in Medical Education: The Measurable Impact of a Humanities-Based Course on Empathy

2025· article· en· W4417332189 on OpenAlexaboutno aff
Maria Giannari, George Botis, Evgenia-Charikleia Lazari, Eirini Thymara, Nikolaos G. Kavantzas, Andreas C. Lazaris

Bibliographic record

VenueJournal of Medical Education and Curricular Development · 2025
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsEmpathyPsychological interventionInterpersonal communicationInterpersonal relationshipSocial skillsIntervention (counseling)

Abstract

fetched live from OpenAlex

Objective: To evaluate the effects of an educational intervention, the elective course "Humanistic Values and Contemporary Medicine," on medical students' empathy levels and to examine the predictive value of demographic and educational variables. Methods: A cross-sectional survey was conducted among 112 medical students using a modified Toronto Empathy Questionnaire assessing empathy in both personal and clinical contexts. Demographic and educational data were collected and analyzed for associations with empathy scores. Results: Most students recognized the importance of empathy, but only a subset had received formal education on the topic. Enrollment in the elective course was significantly associated with higher empathy scores. Gender showed a nearly significant effect, with female students tending to score higher. Other factors, including clinical training, living arrangements, and personal experience with chronic illness, were not significant predictors of empathy. Conclusion: Empathy is amenable to structured educational interventions and should be intentionally cultivated during medical training to support future physicians' interpersonal competencies and emotional resilience.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.023
GPT teacher head0.373
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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