Photodentistry – an innovative approach to improving students’ empathy and learning experiences in comprehensive patient care
Bibliographic record
Abstract
OBJECTIVES: Preparing future dental school graduates to provide comprehensive patient care with empathy requires the completion of adequate training in such practice. This study was undertaken to investigate the effectiveness of the Photodentistry learning activity, which uses visual arts, in improving dental students' empathy and learning experience in comprehensive patient care. METHODS: All fourth-year undergraduate dental students (n = 69, response rate = 100%) participated in the Photodentistry learning activity developed by specialists from the areas of dentistry, arts, education, and psychology. A survey using the Toronto Empathy Questionnaire (TEQ) was conducted both pretest and posttest, followed by an open-ended written survey of their reflection towards the learning activity. Quantitative data were analyzed via paired t-test (P < 0.05), while qualitative data were analyzed using thematic analysis. RESULTS: There was a significant increase in both students' total mean empathy score and the individual scores for 8 (out of 16) items of the TEQ after the learning activity. Students stated that they had an improved understanding of managing patients in a comprehensive manner (e.g., managing medically compromised patients, performing treatment planning, communication with patients who have special health care needs). Students also reported the development of skills (e.g., observation, critical thinking) and positive attitudes (e.g., empathy, responsibility) towards patients. CONCLUSION: Photodentistry is an effective learning approach for improving dental students' empathy and learning experience in comprehensive patient care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".