Empathy level among Saudi medical students using the Toronto empathy scale
Bibliographic record
Abstract
Background: Empathy is a crucial component of professionalism in medicine, having a solid relationship with improved patient outcomes. The current study aims to examine the factor structure of the Toronto Empathy Questionnaire (TEQ) with a sample of Saudi medical students and to assess the differences in empathy scores by gender, year of study, and future career preference. Methods: A cross-sectional study was performed using anonymous self-administered online questionnaires. The study tool targeted a random sample of medical students in public and private Saudi medical schools in five regions (North, South, East, West, and Central) of Saudi Arabia. Results: 941 Saudi medical students enrolled in the study. 52.3% were male students, and 30.6% of the students were from the central region of Saudi Arabia. The most desired specialties were general surgery (19.2%), internal medicine (12.5%), and family medicine 8.2%. The average TEQ score was 42.31%, with 67.1% scoring low to average empathy levels. About one-third (32.9%) scored high empathy levels; females scored a higher average on the empathy score compared to males (43.48 vs. 41.24) P-value <0.001. The never-married studentsalso scored higher empathy than married students 42.53 vs. 38.78) P-value <0.00. The region with the highest empathy scores was the central province, 44.72%. Conclusion: Different factors could influence empathy scores, such as gender, marital status, GPA, and study year. Female students had a higher empathy score compared to male students. Senior medical students scored lower on the scale than younger students, and could be associated with a higher level of burnout. Further empathy-based discussions should be inserted into the Saudi medical curricula. Keywords: professionalism, Toronto empathy scale, medical students, Saudi Arabia.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".