South African men’s perceptions of breast cancer: impact of gender norms on health care accessibility
Bibliographic record
Abstract
Women in low- and middle-income countries (LMICs) often present to the health care system at advanced stages of breast cancer (BC), leading to poor outcomes. A lack of BC awareness and affordability issues are proposed as contributors to the delayed presentation. In many areas of the world, however, women lack the autonomy to deal with their health needs due to restrictive gender norms. The role of gender norms has been relatively underexplored in the BC literature in LMICs and little is known about what men know about BC and how they are involved in women's access to care. To better understand these factors, we conducted a qualitative descriptive study in South Africa. We interviewed 20 low-income Black men with current woman partners who had not experienced BC. Interviewees had limited knowledge and held specific misconceptions about BC symptoms and treatment. Cancer is not commonly discussed within their community and multiple barriers prevent them from reaching care. Interviewees described themselves as having a facilitative role in their partner's access to health care, facets of which could inadvertently prevent their partners from autonomously seeking care. The findings point to the need to better consider the role of the male partner in BC awareness efforts in LMICs to facilitate prevention, earlier diagnosis and treatment.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".