Experiential factors affecting the empathy of students in their pre‐clinical year(s) of 21 universities
Bibliographic record
Abstract
Hands‐on cadaveric dissection is often considered an important factor in shaping the emotional identity of medical and dental students as healthcare providers. This study explores how demographic and/or experiential factors affect the empathy of students in their pre‐clinical year(s) of medical or dental school. In the Summer of 2021 and Fall of 2021, a total of 530 students from 21 universities around the world participating in the International Collaboration and Exchange (ICE) Program, completed a validated questionnaire containing the Santa Clara Brief Compassion (SCBC) Scale and the Toronto Empathy Questionnaire (TEQ). Responses to the SCBC and TEQ were tested for variance and covariance against age group, sex, clinical experience, year of health professional school, format of anatomy education, hours of study on prosections and/or hours of hands‐on cadaveric dissection in their respective curricula; and whether their school provides an opportunity for reflection, information about the body donors, a memorial service, and/or addresses empathy in their curricula. Results show that having 40‐90 hours of hands‐on cadaveric dissection vs 0 hours yielded higher SCBC averages (p = 0.0206) and TEQ scores (p = 0.0031); and having 20‐40 hours of hands‐on cadaveric dissection vs 0 hours also resulted in higher TEQ scores (p = 0.0105). Comparisons of hours of study on prosections, format of anatomy education, clinical experience, and year of health professional school yielded no significant results in relation to empathy scores. Our study found that across different regions of the world, curricula emphasizing dissection are best at preparing students to become more empathetic healthcare providers. While none of the other curricular factors proved significant, this study confirms the merit of hands‐on cadaveric dissection in the emotional development of medical and dental 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".