‘Seeing is believing’ – gender disparities in otolaryngology-head and neck surgery in Africa: a narrative review
Bibliographic record
Abstract
PURPOSE OF REVIEW: Various factors affect otolaryngology - head and neck surgery (OHNS) services in low- and middle-income countries (LMICs); including inadequate infrastructure, limited academic positions, unfavorable hospital research policies, and traditional misconceptions about gender and surgery, among others. Although gender inequalities exist globally, they are particularly pronounced in LMICs, especially in Africa. RECENT FINDINGS: A comparative narrative literature review for relevant manuscripts from January 1, 2017 to through January 10th, 2024, using PubMed, Embase and Google Scholar for articles from the United States/Canada and Africa was done. 195 relevant articles were from the United States/Canada, while only 5 were from Africa and only 1 manuscript was relevant to OHNS. The reviewed articles reported that gender disparities exist in medical training, authorship, and career advancement. We highlight possible solutions to some of these disparities to promote a more gender-diversified workforce in OHNS in Africa as well as all over the world. SUMMARY: Additional studies on gender disparities in Africa, are needed. These studies will highlight need for inclusive policies, structured and accessible mentorship programs; through which these disparities can be highlighted and addressed. This will in the long run ensure sustainability of OHNS care in LMICs.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".