Cross-Sectional Study of HPV Self-Sampling among Indian Women—A Way Forward
Bibliographic record
Abstract
Abstract Molecular human papillomavirus (HPV) DNA is a recommended test for any country planning cervical cancer screening as a national policy. The emerging literature proposes HPV self-sampling (HPV-SS) as a feasible implementing strategy in low-income settings. The success of this strategy would depend on developing impactful health education materials, understanding modalities toward generating awareness, and precision in performing the screening test among beneficiaries. The current paper is an interim analysis of ongoing research undertaken to understand the acceptability of HPV-SS among Indian women across different community settings. The study design has two modalities for generating awareness: (1) health education arm wherein the awareness and steps of collecting self-sample are explained by health personnel, and (2) the pamphlet arm wherein pictorial illustrations depicting the steps to conduct HPV-SS are distributed among women. The quality of samples is compared with primary health worker samples (PHW-S). Initial results of this study support the acceptance of HPV-SS (97%) among urban slum settings. An agreement between HPV-SS and PHW-S was demonstrated to be 95.1%. The results of the pamphlet arm were comparable to the health education arm in every aspect. The art-based strategy seems like a promising communication modality for generating awareness toward cervical cancer screening using HPV-SS in low-resource settings.
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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 | 0.003 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".