What Prevents Men Aged 40–64 Years from Prostate Cancer Screening in Namibia?
Bibliographic record
Abstract
Objectives. Although a growing body of evidence demonstrates the public health burden of prostate cancer in SSA, relatively little is known about the underlying factors surrounding the low levels of testing for the disease in the context of this region. Using Namibia Demographic Health Survey dataset (NDHS, 2013), we examined the factors that influence men's decision to screen for prostate cancer in Namibia. Methods. We use complementary log-log regression models to explore the determinants of screening for prostate cancer. We also corrected for the effect of unobserved heterogeneity that may affect screening behaviours at the cluster level. Results. The results show that health insurance coverage (OR = 2.95, p = 0.01) is an important predictor of screening for prostate cancer in Namibia. In addition, higher education and discussing reproductive issues with a health worker (OR = 2.02, p = 0.05) were more likely to screening for prostate cancer. Conclusions. A universal health insurance scheme may be necessary to increase uptake of prostate cancer screening. However it needs to be acknowledged that expanded screening can have negative consequences and any allocation of scarce resources towards screening must be guided by evidence obtained from the local context about the costs and benefits of screening.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".