Demographic and Economic Predictors of Uptake of Cervical Cancer Screening among Women in Isiolo County, Kenya
Bibliographic record
Abstract
PURPOSE: To determine the demographic and economic factors associated with cervical cancer screening among women in Isiolo County, Kenya. METHODOLOGY: A community based cross-sectional study. The study included 444 women aged 15-65 years drawn from six community units in Isiolo County. Multistage cluster sampling was used to draw a sample from the community units and household levels. Data was collected using a questionnaire administered by a research assistant. The questionnaire consisted of the demographic and economic factors associated with uptake of cervical cancer screening. Descriptive statistics, cross tabulations and multivariate logistic regressions were used in data analysis. FINDINGS: Among the 444 eligible women 81(18.2%) had ever been screened for cervical cancer. The significant determinants of screening included residence (OR=0.012, CI 95% [0.002-0.06] P-p<0.001); education (OR=0.31, CI 95% [0.107-0.895] p<0.05); Occupation (OR=0.142, CI 95% [0.031-0.66] P-Value=0.013); and perception by the respondents that screening is expensive (OR=0.112 CI 95% [0.04-0.309] P-Value<0.001). CONCLUSION: Uptake of cervical cancer screening at Isiolo county is significantly low. This study identified the demographic factors of screening as area of residence and education level. Occupation and the respondents’ opinion that screening is expensive were found to reduce chances of screening among the women. Many of the participants however expressed their willingness to be screened if the service was offered for free. Intensifying health education and community awareness is recommended to equip the women with accurate information regarding cervical cancer and screening.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".