To be or not to be empathic: the combined role of empathic concern and perspective taking in understanding burnout in general practice
Bibliographic record
Abstract
BACKGROUND: General practice is stressful and burnout is common among family physicians. A growing body of evidence suggests that the way physicians relate to their patients could be linked to burnout. The goal of this study was to examine how patterns of empathy explained physicians' burnout. METHODS: We surveyed 294 French general practitioners (response rate 39%), measured burnout, empathic concern (EC) and perspective taking (PT) using self-reported questionnaires, and modeled burnout levels and frequencies with EC, PT and their interaction in linear and logistic regression analyses. RESULTS: Multivariate linear models for burnout prediction were associated with lower PT (β = -0.21, p < 0.001) and lower EC (β = -0.17, p < 0.05). Interestingly, the interaction (EC x PT) also predicted burnout levels (β = 0.11, p < 0.05). The investigation of interactions revealed that high scores on PT predicted lower levels of burnout independent from EC (odd ratios (OR) 0.37; 95% confidence interval (95% CI) 0.21-0.65 p < 0.001), and high scores on both EC and PT were protective against burnout: OR 0.31; 95% CI 0.15-0.63, p < 0.001). CONCLUSIONS: Deficits in PT alone might be a risk factor for burnout, whereas higher PT and EC might be protective. Educators should take into account how the various components of empathy are potentially associated with emotional outcomes in physicians.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".