Association of Physicians’ Self-Compassion with Work Engagement, Exhaustion, and Professional Life Satisfaction
Bibliographic record
Abstract
Self-compassion has shown promise as an adaptive resource for coping with uncertainties and challenges. This study examined the relationship between self-compassion and professional wellbeing (work engagement, exhaustion, and professional life satisfaction) of physicians, who frequently face uncertainties and challenges in their clinical practice. Fifty-seven practicing physicians in Canada participated in the study. Overall, 65% of the participants were female; 47% were in the early-career stage; 49% were family medicine (FM) physicians, with the rest being non-FM specialists. It was hypothesized that (a) self-compassionate physicians would experience greater work engagement and less exhaustion from work than physicians reporting lower self-compassion and (b) self-compassionate physicians would experience greater professional life satisfaction through their greater work engagement and less exhaustion than physicians reporting lower self-compassion. Sequential regression analyses were performed. The results confirmed the hypothesized associations, indicating that self-compassionate physicians experienced more positive work engagement, felt less emotionally, physically, and cognitively exhausted due to work demands, and were more satisfied with their professional life than physicians who exhibited less compassion toward themselves in uncertain and challenging times. Future studies are needed to determine optimal ways to support practicing physicians and medical trainees in becoming more self-compassionate for their enhanced wellbeing and, ultimately, for the provision of effective 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".