Effect of a Person‐Centered Course on the Empathic Ability of Dental Students
Bibliographic record
Abstract
Person-centered or patient-centered care (PCC) focuses on the individual's needs and concerns. Although PCC is widely acknowledged as a core value of modern medicine, there has been a lack of research on how dental curricula could engage future dentists in PCC approaches. The aim of this study was to assess the impact of a PCC course on empathy in dental students. A controlled study was conducted with fourth-year dental students in four dental faculties in France in 2014-15. The test group (n=63) received 20 hours of PCC training including arts-based approaches, narrative dentistry activities, and workshops on communication based on the Calgary-Cambridge guide. There was no change in the curriculum of the control group (n=217). Pretest and posttest measures with the Toronto Empathy Questionnaire (TEQ) and Jefferson Scale of Physician Empathy (JSPE) were compared for the two groups. The comparisons showed no significant differences on the TEQ or JSPE (p=0.25 and p=0.08, respectively). However, there was a higher proportion of students with more than an eight-point decrease in TEQ values in the control group (p=0.02). The stabilization of empathic ability in the test group may have counteracted the tendency for natural erosion of empathy among students during their clinical activities. These results suggest that PCC training constitutes a promising approach to developing dental students' empathic ability, but there is a need to assess the effects of such training over longer periods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".