Evaluation of Dental Student Empathy Scores Using Cognitive and Effective Empathy Measures
Bibliographic record
Abstract
BACKGROUND: Empathy is stressed as an important component of patient-centered practice and training within the dental profession, yet assessment of both cognitive and affective empathy among dental students in a US dental school model is sparse. AIM: To determine if dental students' cognitive and affective empathy differs by class year, gender, and age in a US dental school model. METHODS: This cross-sectional study examined self-reported empathy levels of first to fourth year dental students from a US dental school, using the Jefferson Scale of Empathy-Health Profession Students (JSE-HPS) and the Toronto Empathy Questionnaire (TEQ). Questions were scored using a 1-7 Likert scale (1 = strongly disagree, 7 = strongly agree); associations were tested using ANOVA and two-sample t-tests. RESULTS: Two-thirds of the dental students (66%) took the survey, n = 280. Most were female (50%), between 19 and 24 years old (52%), and in their first or second year of dental school (66%). The average JSE-HPS score was 112 ± 11 and the average TEQ score was 91 ± 11. JSE-HPS scores were significantly different between dental classes (p = 0.012). Fourth year students had significantly lower JSE-HPS scores than the first and third year students (108 vs. 114 and 114; Tukey's p = 0.022 and 0.044). Females and students < 25 years demonstrated significantly higher empathy both JSE-HP and TEQ. Cognitive and affective empathy measures were positively associated (Pearson r = 0.63, p < 0.001). CONCLUSION: Dental educators should be aware of changes in student empathy levels during dental training to support and promote empathy development across class years within the US dental school model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".