Gender differences in the effect of grief reactions and burnout on emotional distress among clinical oncologists
Bibliographic record
Abstract
BACKGROUND: The current study was conducted to examine gender differences in the effect of grief reactions and burnout on emotional distress among clinical oncologists. METHODS: The participants included a convenience sample of 178 oncologists from Israel (52 of whom were women) and Canada (48 of whom were women). Oncologists completed a questionnaire package that included a sociodemographic survey, the General Health Questionnaire, a burnout measure, and the Adult Oncologists Grief Questionnaire. To examine the effect of grief reactions and burnout on emotional distress while controlling for country and past depression within each gender, 2 hierarchical linear regression analyses were computed. RESULTS: Female oncologists reported significantly more grief responses to patient death (mean, 47.72 [standard deviation (SD), 8.71] and mean, 44.53 [SD, 9.19], respectively), more emotional distress (mean, 12.41 [SD, 4.36] and mean, 10.64 [SD, 3.99], respectively), and more burnout (mean, 2.59 [SD, 1.69] and mean, 1.84 [SD, 1.5], respectively). For both genders, higher levels of grief reactions were associated with greater emotional distress among those who reported high levels of burnout (P<.001). However, for men, the association between grief reactions and emotional distress also was documented at moderate levels of burnout (P<.001). CONCLUSIONS: Patient death is a regular part of clinical oncology. It is essential that oncologists be able to cope effectively with this aspect of their work. The findings of the current study highlight the need to take into account the cumulative stressors that oncologists contend with when designing supportive interventions. Gender differences in burnout, reactions to patient death, and emotional distress need to be addressed to ensure the best quality of life for oncologists and the best quality of care for their patients. Cancer 2016;122:3705-14. © 2016 American Cancer Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".