Does Prior Experience Matter? Intention to Undergo Cervical Cancer Screening among Rural Women in South-Central Ethiopia
Bibliographic record
Abstract
Early screening for cervical cancer has substantially reduced the morbidity and mortality attributed to it. This study aimed to assess factors that affect the intention to undergo cervical cancer screening among rural women attending primary healthcare facilities in south-central Ethiopia. A health-facility-based, cross-sectional study design was employed for which the calculated required sample size was 427. An interviewer-administered structured questionnaire was adapted from previously published research and used to collect data. Statistical Package for Social Sciences (SPSS) version 27 was used for the statistical analysis. A logistic regression model was used to determine the factors that influenced the women's intention to undergo cervical cancer screening. A total of 420 women participated in this study, with a response rate of 98%. The mean score from the questionnaire that was used to assess the women's intention to undergo cervical cancer screening was 10.25 (SD ± 2.34; min 3, max 15). The absence of previous screening experience (AOR: 0.498; 95% CI 0.27-0.92) and high degree of perceived behavioural control (AOR, 0.823; 95% CI 0.728-0.930) were significantly negatively associated with women's intention to undergo cervical cancer screening. Previous screening experience and perceived behavioural control significantly influenced the intention to undergo cervical cancer screening. Women in rural areas could, therefore, benefit from awareness-creation programmes that focus on these factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".