Challenges faced by women oncologists in Africa: a mixed methods study
Bibliographic record
Abstract
Objective: Recent studies have identified challenges facing women oncologists in Western contexts. However, similar studies in Africa have yet to be conducted. This study sought to determine the most common and substantial challenges faced by women oncologists in Africa and identify potential solutions. Methods and analysis: A panel of 29 women oncologists from 20 African countries was recruited through professional and personal networks. A Delphi consensus process identified challenges faced by women oncologists in Africa, and potential solutions. Following this, focus group discussions were held to discuss the results. Descriptive statistics were used to identify the most common challenges indicated by participants and thematic analysis was conducted on focus group transcripts. Results: African women oncologists experienced challenges at individual, interpersonal, institutional and societal levels. The top-ranked challenge identified in the Delphi study was 'pressure to maintain a work-family balance and meet social obligations'. Some of the challenges identified were similar to those in studies on women oncologists outside of Africa while others were unique to this African demographic. Solutions to improve the experience of women oncologists were identified and discussed, including greater work flexibility and mentorship opportunities. Conclusion: Women oncologists in Africa experience many of the challenges that have been previously identified by studies in other regions. These challenges and potential solutions exist at all levels of the social-ecological framework. Women oncologists must be empowered in number and leadership, and gender-sensitive curricula and competencies must be implemented. A systems-level dialogue could bring light to these challenges and foster tangible action and policy-level changes.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".