The Cross-Sectional Study for Comparison of Empathy-based on Competency-based Curriculum among Indian Undergraduates
Bibliographic record
Abstract
Introduction: Competency-based medical education (CBME) was introduced by the National Medical Commission in 2019. The system tries to incorporate the Attitude, Ethics, and Communication (AETCOM) module for the enhancement of empathy, cognition, and soft skill development in undergraduates (UGs). Longitudinal and comparative studies in this area show mixed findings regarding response to CBME. Therefore, this study aims to compare empathy in UGs before and after the introduction of a new curriculum and assess the predictors for the same. Methods: The empathy was assessed and compared cross-sectionally among 700 UGs, belonging to both old and new curricula, with the Toronto empathy questionnaire (TEQ). Results: A course of fluctuating levels was observed in empathy for UGs with advancement of MBBS years followed by a dip in the end. Females (47.52 ± 6.00) had more empathy scores than males (42.97 ± 7.55) with significance (p < 0.05). There was no comparable rise in empathy scores with the introduction of a new curriculum. Gender proved significant in predicting empathy with multiple linear regression (p < 0.05) in both CBME and non-CBME students. Conclusion: The nurture of empathy starts during early medical education. The students had a decrease in empathy scores at the end of medical training, with females having higher empathy levels. The new curriculum tries to inculcate empathic communication for better care but needs further evaluation on causal factors and data on longitudinal trends.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".