Breast cancer screening among women in Namibia: explaining the effect of health insurance coverage and access to information on screening behaviours
Bibliographic record
Abstract
OBJECTIVES: Breast cancer contributes substantially to morbidity and mortality in Namibia as is the case in most countries in Sub-Saharan Africa (SSA). However, there is a dearth of nationally representative studies that examine the odds of screening for breast cancer in Namibia and SSA at large. This paper aims to fill this gap by examining the determinants of breast cancer screening guided by the Health Belief Model. METHODS: We applied hierarchical binary logit regression models to explore the determinants of breast cancer screening using the 2013 Namibia Demography and Health Survey (NDHS). We accounted for the effect of unobserved heterogeneity that may affect breast cancer, testing behaviours among women cluster level. The NDHS is a nationally representative dataset that has recently started to collect information on cancer screening. RESULTS: ≤ 0.01) education were more likely to be screened for breast cancer. Factors that influence women's perception of their susceptibility to breast cancer such as birthing experience, age, region and place of residence were associated with screening in this context. CONCLUSIONS: Overall, the health belief model predicted women's testing behaviours and also revealed the absence of relevant risk factors in the NDHS data that might influence screening. Overall, our results show that strategies for early diagnosis of breast cancer should be given major priority by cancer control boards as well as ministries of health in SSA. These strategies should centre on early screening and may involve reducing or eliminating barriers to health care, access to relevant health information and encouraging breast self-examination.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".