Can a virtual learning module foster empathy in dental undergraduate students?
Bibliographic record
Abstract
BACKGROUND: Empathy is an essential part of patient-centred health care, which positively benefits both patients and clinicians. There is little agreement regarding how best to design and deliver training for healthcare trainees to impart the skills and behaviours of clinical empathy. The study aimed to inform the field by sharing an educational intervention where we aimed to improve empathy amongst dental undergraduate students in Trinity College Dublin using a virtual learning module. METHODS: Adopting pre-post-repeat pre-experimental design, dental professional students completed the Jefferson Scale of Empathy (JSE) for Health Professional Students immediately prior to and after a three-week virtual programme designed to increase clinical empathy. Using a three-factor model described for the JSE in the literature, scores were evaluated for internal consistency and paired tests were performed on scores appropriate to their distributions. Seven-point Likert scales were scored to record student experience of training and technology, which are reported descriptively. RESULTS: Most of the 37 participants were female (76%) and represented dental science (N = 27) and dental hygiene roles (N = 7). Results revealed a mean JSE-HPS scale score rise from 110.0 (SD = 10.4) to 116.4 (SD = 11.1), which represented a rise of 5.8% (t (36) = 3.6, p = 0.001). The three factors associated with cognitive empathy, namely perspective-taking (T(36) = 3.931, p < 0.001; walking in the patient's shoes T(36) = 2.093, p = 0.043); and compassionate care (Z = 2.469, p = 0.014) were all found to have increased after the intervention. Students reported a positive experience of discipline-specific and generic videos as part of the module. CONCLUSION: The study demonstrated that a virtual educational module was associated with an increase in empathy amongst dental undergraduate students. The design of a blended module incorporating the Massive Open Online Course (MOOC) and virtual learning are beneficial and have a promising future.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".