Burnout among oncologists and oncology nurses: A systematic review and meta-analysis.
Bibliographic record
Abstract
BACKGROUND: Significant proportions of burnout have been reported among both oncologists and oncology nurses. However, these groups have not been compared in a meta-analytic design. It is important to compare how burnout affects different types of health professionals to understand its individual implications and devise ways of minimizing and treating it. OBJECTIVE: The current meta-analysis study aimed to systematically compare burnout prevalence between oncologists and oncology nurses. METHOD: Authors assessed 34 studies (four included nurses and oncologists and 30 focused either on oncologists or oncology nurses) that used the Maslach Burnout Inventory (MBI) to measure burnout. Both fixed- and random-effects models were used to calculate meta-analytic estimates of the burnout subscales: emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA). RESULTS: The pooled sample size was 4,705 oncologists and 6,940 oncology nurses. The average proportions of EE, DP, and PA were 32%, 26%, and 25%, respectively, among oncologists and 32%, 21%, and 26%, respectively, among oncology nurses. Higher DP was found among oncologists compared with oncology nurses, only in the analysis of studies that included samples of both oncologists and oncology nurses. The subgroup analysis showed higher levels of DP in Europe and Asia and lower PA in Asia and Canada. No evidence of publication bias was found. CONCLUSIONS: Findings suggest differences in burnout between oncologists and oncology nurses and among geographic regions. This highlights the need for tailored interventions for different professions and regions. Hospitals should provide support and encourage teamwork to improve oncology professionals' well-being and provide optimal care for patients. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.027 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads agree on what is shown here.
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".