Cultivating physician empathy: a person-centered study based in self-determination theory
Bibliographic record
Abstract
While physician empathy is a vital ingredient in both physician wellness and quality of patient care, consensus on its origins, and how to cultivate it, is still lacking.The present study examines this issue in a new and innovative way, through the lens of self-determination theory.Using survey methodology, we collected data from N = 177 (44%) students at a Canadian medical school.We then used a person-centered approach (cluster analysis) to identify medical student profiles of self-determination (based on trait autonomy and perceived competence in learning) and how the learning environment impacted empathy for those in each profile.When the learning environment was more autonomy-supportive, students experienced higher satisfaction and lower frustration of their basic psychological needs in medical school, as well as greater empathy towards patients.The translation into increased empathy, however, was only evident among the students with higher self-determination at baseline.Results from this study suggest that autonomy-supportive learning environments will generally support medical students' psychological needs for optimal motivation and well-being, but whether or not they lead to empathy towards patients will depend on individual differences in self-determination.Findings and their implications are discussed in terms of developing theory-driven approaches to cultivating empathy in medical education.
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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 | 0.011 | 0.012 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".