Prevalence and risk factors of burnout among female oncology professionals from the Middle East and North Africa (MENA).
Bibliographic record
Abstract
11016 Background: Burnout (BO) is a recognized challenge among oncology workforce. It affects both genders with a higher frequency among women. This study examined the factors contributing to the development of burnout among women in oncology from the Middle East and North Africa (MENA). Methods: An online cross-sectional survey was distributed to oncology professionals from different countries in the MENA region. The validated Maslach Burnout Inventory (MBI) of emotional exhaustion (EE), Depersonalization (DE), and Personal Achievement (PA) plus questions about demography/work-related factors and attitudes toward oncology were included. Data were analyzed to measure BO prevalence and related factors. Results: Between February 10 and March 15, 2020, 545 responses were submitted by female professionals. The responses pre-dated the COVID-19 pandemic emergence in the region. BO prevalence was 71% among female professionals. Women aged < 44 years represented 85% of the cohort. Sixty-two percent were married, 52% with children and one-third practiced a hobby. Two-thirds worked in medical oncology, worked for < 10 years and 35% worked in academia. The majority (73%) spent > 25% on administrative work daily. Nearly half of the respondents (49%) expressed a recurring thought of quitting oncology and 70% had no burnout support or education. Inability to deliver optimal care was reported as distressing for career development in 82%. Factors significantly influencing the BO risk are listed in Table. Marital status, having children, academia and years in practice did not impact the risk of BO among female oncologists form MENA. Conclusions: High BO prevalence was reported among female oncology professionals from MENA. The majority of women oncology workforce were young and early- to mid-career in this cohort. Younger age, practicing in North African countries, high administrative load and the recurring thought of quitting were associated with increased risk of burnout. Whereas, practicing a hobby and enjoying oncology communication decreased the BO risk. Burnout support and education specifically for women in oncology is needed.[Table: see text]
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".