Medical studentsâ perspectives on clinical empathy training
Bibliographic record
Abstract
Context: There is a need for studies specifically addressing the barriers to empathy training from the perspective of medical students. The objective of this study was to evaluate attitudes of 3rd and 4th year medical students regarding their training in clinical empathy at a public teaching hospital and medical school. Methods: A questionnaire assessing students’ satisfaction with, and opinions on, empathy training, as well as barriers to training, was distributed during the last quarter of the year. Results: Of 188 eligible participants, 157 (84%) responded. Approximately one-half of the respondents said empathy could be taught. Eighty-one percent of respondents felt that their empathy had increased or stayed the same during their training. When asked about barriers for learning empathy, the majority of respondents chose time pressure and lack of good role models. Respondents rated breaking bad news, talking to patients about medical mistakes and taking care of dying or demanding patients as areas in need of more empathy-related training. Conclusions: Although the majority of students were satisfied with their training of clinical empathy, our study highlights the need for innovative methods to address concerns regarding barriers to practicing empathy, as well as the need for more training in how to demonstrate empathy in challenging clinical situations. ©B Afghani, S Besimanto, A Amin, J Shapiro, 2011.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".