Are alexithymia and empathy predicting factors of the resilience of medical residents in France?
Bibliographic record
Abstract
Objectives: To explore resilience, resilience predicting factors and resilience distribution in French medical residents.Methods: A cross-sectional study was conducted in which general practice residents (n = 380) were asked to answer the Jefferson Scale of Physician Empathy, the Connor-Davidson Resilience Scale, and the Toronto Alexithymia Scale.One hundred thirty-seven (137) responses were collected.The scores of the different scales have been calculated.The score differences were examined using the Student's t-test or analysis of variance.The correlations were estimated using the Pearson correlation coefficient.The relationships between scores were analysed by multiple linear regression.The heterogeneity of the sample was examined by non-hierarchical cluster analysis.Results: Resilience and empathy were positively correlated (r(135) = .36,p < .001).Alexithymia was negatively correlated with resilience, r(135) = -.40,p<.001, and empathy, r(135) = -.38,p<.001.Resilience was influenced by alexithymia, β = -.284,p = .001,empathy, β = .255,p = .002,gender (female < male), β = -.231,p = .002and year of formation, β= .157,p = .036.Two clusters of residents were characterized.They differed by their empathy and resilience profiles and by alexithymia trait.Conclusions: Alexithymia, empathy, gender and year of formation correspond to predicting factors of resilience.This suggests that the resilience of vulnerable residents can be enhanced by increasing their empathy and by reducing their alexithymia.Thus, teaching teams could sustain their students' well-being through educational programs aiming to develop their understanding of their own emotions and those of their patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".