Acceptability of cervical cancer screening using visual inspection among women attending a childhood immunization clinic in Uganda
Bibliographic record
Abstract
OBJECTIVE: To evaluate the acceptability and performance of cervical cancer (CC) screening using visual inspection with acetic acid (VIA) integrated into a rural immunization clinic in Uganda. METHODS/MATERIALS: We conducted a cross-sectional pilot study in rural Uganda. We explored associations between women's characteristics and acceptance of VIA testing. We collected samples for Papanicolaou (Pap) smear testing in a random subset of women and used results from this test as a comparator for assessing VIA performance. RESULTS: We enrolled 625 women of whom 571 (91.4%) accepted and 54 (8.6%) refused CC screening. In the univariate model, age (Odds Ratio (OR)=1.10; p-value<0.001) and employment status (OR 2.00; p-value=0.019) were significantly associated with acceptance of VIA screening. In the multivariate model, no characteristic was independently associated with acceptance of VIA screening after adjusting for other factors. Compared to reference Pap smear, CC screening with VIA had a sensitivity of 50% and a specificity of 97.7%. CONCLUSIONS: CC screening with VIA is highly acceptable in the setting of rural immunization clinics in Uganda. Studies to assess which screening method would be the most effective and cost-effective are needed before stakeholders can consider adopting screening programs at scale.
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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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".