Examining the availability and readiness of health facilities to provide cervical cancer screening services in Nepal: a cross-sectional study using data from the Nepal Health Facility Survey
Bibliographic record
Abstract
OBJECTIVE: We assessed the availability and readiness of health facilities to provide cervical cancer screening services in Nepal. DESIGN: Cross-sectional study. SETTING: We used secondary data from a nationally representative 2021 Nepal Health Facility Survey, specifically focusing on the facilities offering cervical cancer screening services. OUTCOME MEASURES: We defined the readiness of health facilities to provide cervical cancer screening services using the standard WHO service availability and readiness assessment manual. RESULTS: The overall readiness score was 59.1% (95% CI 55.4% to 62.8%), with more equipment and diagnostic tests available than staff and guidelines. Public hospitals (67.4%, 95% CI 63.0% to 71.7%) had the highest readiness levels. Compared with urban areas, health facilities in rural areas had lower readiness. The Sudurpashchim, Bagmati and Gandaki provinces had higher readiness levels (69.1%, 95% CI 57.7% to 80.5%; 60.1%, 95% CI 53.4% to 66.8%; and 62.5%, 95% CI 56.5% to 68.5%, respectively). Around 17% of facilities had trained providers and specific guidelines to follow while providing cervical cancer screening services. The basic healthcare centres (BHCCs) had lower readiness than private hospitals. Facility types, province and staff management meetings had heterogeneous associations with three conditional quantile scores. CONCLUSION: The availability of cervical cancer screening services is limited in Nepal, necessitating urgent action to expand coverage. Our findings suggest that efforts should focus on improving the readiness of existing facilities by providing training to healthcare workers and increasing access to guidelines. BHCCs and healthcare facilities in rural areas and Karnali province should be given priority to enhance their readiness.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".