Empathy Levels in Medical Students: A Single Center Study
Bibliographic record
Abstract
Objectives To determine the level of empathy in medical students and to determine the difference in empathy levels between the two genders in a single center. Materials & methods This qualitative study was conducted at a medical college in Peshawar from March 2021 to July 2021. Institutional ethical committee approval was taken (RMI/RMI-REC/Approval/83) before commencing the study. All students admitted into the medical college in the current academic year 2020 to 2021 were included in the study. Any students that did not fill out the questionnaire completely were excluded. The Toronto Empathy Questionnaire (TEQ) was used in this study. The questionnaire was uploaded on google forms for data collection. All the resulting scores were entered into IBM SPSS version 23.0. The mean TEQ score was calculated. Box and whisker plots were made for respective years. An Independent sample t-test was used to determine the association between mean TEQ scores and gender. Results Of 367 students, 347 (94.6%) participated in this study, with a slight female predominance (53%). The mean age of the students was 21.44 (SD = 1.751) years. The participation rate was ≥70% from each class. Most participants across the years have an above-average empathy score (49.9%). Among the participants, the year I (67.6%) showed most participants with high empathy. Year IV (40.6%) has the highest proportion of below-average empathy scores. The mean empathy score of female students was 49.08 (S.D = 7.588), while the empathy score for male students was 44.59 (S.D = 7.58). Conclusion Empathy levels decline as medical education is progressed through the years. Females show a greater sense of empathy than their male counterparts. A slight increase in empathy levels is seen in the final year of medical school after a decline over the initial years.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".