Breast cancer screening among women in Namibia: explaining the effect of health insurance coverage and access to information on screening behaviours
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it