Challenges faced by female oncologists in sub-Saharan Africa.
Bibliographic record
Abstract
11001 Background: Recent articles by ASCO and ESMO have identified challenges facing female oncologists in western contexts. The challenges female oncologists face in sub-Saharan Africa (SSA) have yet to be explored. This study was launched by the AORTIC Education and Training Committee to determine the most common and substantial challenges faced by female oncologists in SSA and identify potential solutions. Methods: A diverse panel of 32 female oncologists from 20 countries in SSA was recruited through professional and personal networks. Following an initial meeting to review terminology, a modified three-round Delphi process took place. Participants iteratively reviewed a list of previously identified challenges facing women in oncology in SSA and identified new challenges. The survey was conducted via REDCap, a secure-web-based software platform. Participants reflected on personal experiences or those of colleagues, and were asked to indicate their agreement with each listed challenge, as well as propose solutions. Descriptive statistics identified the most common challenges. Following the third survey, a focus group was held to enrich study data. A thematic analysis is being conducted on the focus group transcript to identify key themes, and a subsequent modified Delphi process is being executed to build consensus around potential solutions to identified challenges. Results: Response rates for the 3 modified Delphi rounds were 66%, 66%, and 53%. The challenge with the greatest agreement was, “pressure to maintain a work-family life balance and meet social obligations”. These were felt to be unique to women in SSA due to an extended family network with several responsibilities beyond the nuclear family. The next two top-scored challenges were “lack of female support and networks”, and “micro-aggressions” (Table). Conclusions: Female oncologists in SSA experience many of the challenges that have been previously identified by similar studies in other regions, with different degrees of perceived importance. Some challenges have a different lived experience for female oncologists in SSA. The second part of this study will include thematic analysis of the recent focus group and explore potential solutions to mitigate these challenges, which will add insight and potential paths forward to optimizing a diverse workforce in SSA.[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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".