Burnout in oncology: Magnitude, risk factors and screening among professionals from Middle East and North Africa (BOMENA study)
Bibliographic record
Abstract
BACKGROUND: Burnout (BO) among oncology professionals (OP) is increasingly being recognized. Early recognition and intervention can positively affect the quality of care and patient safety. This study investigated the prevalence, work and lifestyle factors affecting BO among OPs in the Middle East and North Africa (MENA). METHODS: An online survey was conducted among MENA OPs between 10 February and 15 March 2020, using the validated Maslach Burnout Inventory of emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA), including questions regarding demography/work-related factors and attitudes towards oncology. Data were analysed to measure BO prevalence and risk factors and explore a screening question for BO. RESULTS: Of 1054 respondents, 1017 participants (64% medical oncologists, 77% aged less than 45 years, 55% female, 74% married, 67% with children and 40% practiced a hobby) were eligible. The BO prevalence was 68% with high levels of EE and DP (35% and 57% of participants, respectively) and low PA scores (49%). BO was significantly associated with age less than 44 years, administrative work greater than 25% per day and the thought of quitting oncology (TQ). Practising a hobby, enjoying oncology communication and appreciating oncology work-life balance were associated with a reduced BO score and prevalence. North African countries reported the highest BO prevalence. Lack of BO education/support was identified among 72% of participants and TQ-predicted burnout in 77%. CONCLUSIONS: This is the largest BO study in MENA. The BO prevalence was high and several modifiable risk factors were identified, requiring urgent action. TQ is a simple and reliable screening tool for BO.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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 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".