Factors associated with cervical cancer screening among women of reproductive age in Ghana
Bibliographic record
Abstract
BACKGROUND: Cervical cancer remains a leading cause of cancer-related deaths among women in Ghana and other Sub-Sahara African (SSA) countries. Despite the importance of early diagnosis for timely treatment and death prevention, cervical cancer screening among women in developing countries remain very low. Nonetheless, there is a paucity of research examining the factors associated with screening uptake among women of reproductive age in Ghana. Thus, this study fills the scholarly void and contributes to the existing literature by examining the determinants of cervical cancer screening in Ghana. METHODS: Utilizing data from the 2022 Ghana Demographic and Health Survey (GDHS) (N = 15,014 women), and by employing logistic regression models for a cross-sectional analysis, this study evaluated the factors associated with cervical cancer screening in Ghana. RESULTS: Women with tertiary educational attainment (OR = 4.140; 95%CI: 2.960 5.789; p < 0.001), from the richer (OR = 1.968; p < 0.001) and richest households (OR = 2.492; p < 0.001), the married/living with partner (OR = 1.773; 95%CI:1.372 2.290; p < 0.001), the widowed/divorced/separated (OR = 1.888; 95%CI:1.320 2.701; p < 0.001), owners of valid health insurance card (OR = 1.356; 95%CI:1.086 1.693; p < 0.01), visitation to health facility in the past 12 months (OR = 1.312; 95%CI: 1.082 1.590; p < 0.001), those who watched television at least once in a week (OR = 1.395; 95%CI: 1.055 1.846; p < 0.001), as well as those who listened to radio at least once in a week (OR = 1.509; 95%CI: 1.228 1.853; p < 0.001), were all significantly more associated with cervical cancer screening in the study context. Also, ethnicity, religion, and the region of residence significantly predicted cervical cancer screening in the study context. CONCLUSION: Cervical cancer screening in Ghana can be improved by addressing socioeconomic and geographical disparities in the country's healthcare system. To ensure early detection, timely treatment or care and prevention of cervical cancer-related deaths in the country, there must be coordinated efforts by the government of Ghana to improve healthcare access and surveillance systems for cervical cancer cases, particularly, in geographically disadvantage areas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".