Impact of Multimodal Intervention on Empathy Levels in Medical Students: A Questionnaire-Based Study
Bibliographic record
Abstract
Background Empathy is essential for effective doctor-patient communication. It enables doctors to understand patients' emotions and concerns, facilitating personalized care and support. Empathy can be cultivated through various methods and training programs. Objective The study aims to assess the effectiveness of a multimodal intervention involving interactive lectures, peer role-play, and guided reflection in enhancing empathy levels among second-year medical undergraduate students in India. Methods This study utilized a questionnaire-based, pre- and post-test interventional design. Seventy-nine second-year medical students were included after obtaining their informed consent. The students received the intervention through an interactive lecture on communication skills, role-play on selected case studies, and guided reflection. The empathy levels were assessed using the Toronto Empathy Questionnaire (TEQ) before and after the intervention. The Mann-Whitney U test was utilized to compare pre-test and post-test TEQ scores. A univariate analysis of variance was conducted to explore the relationship between demographic variables and post-test TEQ scores. Statistical significance was considered at p ≤ 0.05. Results The TEQ score improved significantly (p=0.009) after the intervention. The univariate analysis indicated that gender, style of education, and place of residence did not have a statistically significant impact on post-test scores. Conclusion The study demonstrates that a multimodal intervention significantly enhances the empathy level of medical students, highlighting the potential of focused interventions to reduce gender disparities in empathy levels. There were no significant differences in empathy scores based on gender, place of residence, or schooling, suggesting the intervention's benefits may apply to all medical students.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".