Utilization of breast cancer screening in Kenya: what are the determinants?
Bibliographic record
Abstract
BACKGROUND: Breast cancer accounts for 23% of all cancer cases among women in Kenya. Although breast cancer screening is important, we know little about the factors associated with women's breast cancer screening utilization in Kenya. Using the Andersen's behavioural model of health care utilization, we aim to address this void in the literature. METHODS: We draw data on the Kenya Demographic and Health Survey and employ univariate, bivariate, and multivariate analyses. RESULTS: We find that women's geographic location, specifically, living in a rural area (OR = 0.89; p < 0.001) and the North Eastern Province is associated with lower odds of women being screened for breast cancer. Moreover, compared to the more educated, richer and insured, women who are less educated, poorer, and uninsured (OR = 0.74; p < 0.001) are less likely to have been screened for breast cancer. CONCLUSION: Based on these findings, we recommend place and group-specific education and interventions on increasing breast cancer screening in Kenya.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".