Stress Factors Associated With Burnout Among Attending Physicians: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Burnout in attending physicians is a crucial issue that may negatively impact patient outcomes, as well as affect the quality of training provided to residents. To investigate the association between burnout and stress-coping ability, we conducted a cross-sectional study of attending physicians. METHODS: From April 2013 to March 2014, we distributed an anonymous, self-administered questionnaire to 1,897 attending physicians who attended teaching-related training sessions and workshops. The questionnaire included the Maslach Burnout Inventory General Survey (MBI-GS, Japanese version) to evaluate burnout; the sense of coherence scale (SOC, Japanese version) to measure stress-coping ability, with higher scores indicating higher stress-coping ability; the Brief Scales for Job Stress (BSJS) to assess stress and buffering factors; demographic factors; mean weekly working hours; and factors related to instructing residents. The MBI-GS was used to determine the presence of physician burnout. Subjects were divided into tertiles based on SOC scores. We conducted logistic regression analysis of burnout using the following independent variables: physician experience, sex, mean weekly working hours, SOC group, mental workload, and reward from work. RESULTS: Of the 1,543 (81.3%) attending physicians who responded, 376 did not meet the inclusion criteria and 106 had missing data, thus 1,061 (55.9%) were analyzed. The prevalence of burnout was 17.2%. Physicians with burnout had significantly fewer years of experience as a doctor (P < 0.01), were more likely to be female (P < 0.01), worked more hours per week (P < 0.01), and had a lower SOC score (P < 0.01) than physicians without burnout. On the BSJS, the mean score of all stress factors was higher and that of buffering factors was lower in physicians with burnout (P < 0.01). The percentages of physicians with burnout were 35.7%, 12.8%, and 3.2% in the low, middle, and high SOC groups, respectively (P < 0.01). Using the high SOC group as a reference, the adjusted odds ratio for burnout in the low SOC group was 4.7 (95% confidence interval: 2.31 - 9.63) (P < 0.01). CONCLUSIONS: In this study, burnout among attending physicians was significantly associated with SOC scores after adjustment for stress factors and buffering factors.
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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 | 0.001 |
| 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) | 0.001 | 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".