Factors Associated With Low Utilization of Cervical Cancer Screening Services in Gazipur, Bangladesh
Bibliographic record
Abstract
Background: Despite its availability, cervical cancer screening services continue to remain underutilized in many regions. This study aimed to assess the prevalence and determinants of cervical cancer screening uptake among women in the north-central area of Bangladesh. Methods: In this cross-sectional study, between May and October 2022, women aged 30-60 years attending a tertiary care hospital in Gazipur district were approached for inclusion. Face-to-face interviews were conducted using a semistructured questionnaire. A total of 252 women were consecutively recruited within the study period. The self-reported screening practice was recorded and verified by matching with identification numbers provided for screening by the hospital, and reasons for nonutilization were also collected. Results: Only 12 women (4.76%) had ever been screened for cervical cancer. Lower knowledge scores (OR: 0.26 and 95% CI: 0.08-0.95) were associated with higher odds of nonutilization of cervical cancer screening services on multivariable analysis. Despite high awareness of symptoms and risk factors, only 15.08% knew that screening prevents cancer. The main reasons for not getting screened were fear of pain (98.33%) and feeling shy (52.50%). Conclusion: Awareness-increasing programs are recommended to improve the utilization of cervical cancer screening among women.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".