Addressing physician quality of life: understanding the relationship between burnout, work engagement, compassion fatigue and satisfaction
Bibliographic record
Abstract
Burnout and compassion fatigue are now recognized as occupational hazards associated with the medical profession. Interestingly, burnout and compassion fatigue do not occur in every physician and many continue to find joy, meaning and satisfaction in their work despite its challenges and stressors. Our study looked at the relationship between burnout, work engagement, compassion fatigue and satisfaction amongst doctors. We also studied the relationship between these and four measureable intrinsic human factors; self-efficacy, resilient personality type, sense of gratitude and work calling. Our study found that 37% of the doctors were at high risk of burnout and 7.5% were at high risk of compassion fatigue and only 3.3% and 1.5% were at low risk of burnout and compassion fatigue respectively. Only 2.7% and 0.3% had high rates of work engagement and compassion satisfaction respectively. There was a mild but significant negative correlation between burnout and engagement, and a poor negative correlation between compassion fatigue and satisfaction. Only intrinsic human factors were significantly correlated to burnout, work engagement, compassion fatigue and satisfaction. Our preliminary findings suggest that certain intrinsic factors increase work engagement and compassion satisfaction amongst doctors. As some of these intrinsic factors also appear to buffer against burnout and compassion fatigue, increasing work engagement and compassion satisfaction not only builds individual resilience against burnout and compassion fatigue but may also lead to improvement in overall health, professional quality of life and career longevity for doctors.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| 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".