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Record W4284987983 · doi:10.1093/heapro/daac075

South African men’s perceptions of breast cancer: impact of gender norms on health care accessibility

2022· article· en· W4284987983 on OpenAlexafffund
Raquel Burgess, Brown Lekekela, Ruari‐Santiago McBride, John Eyles

Bibliographic record

VenueHealth Promotion International · 2022
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsHealth careMedicineBreast cancerAutonomyQualitative researchPerceptionNursingFamily medicinePsychologyCancerPolitical scienceSociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.115
GPT teacher head0.461
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes2
Has abstractyes

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