Novel Strategy of Training the Accredited Social Health Activists via Application-Based Telemedicine for Cervical Cancer Screening by Human Papillomavirus Self-Sampling: The TRACK Study
Bibliographic record
Abstract
PURPOSE: This pilot study is aimed to analyze a novel strategy of cervical cancer screening by training of Accredited Social Health Activist (ASHA) workers via telemedicine to counsel women for human papillomavirus (HPV) self-sampling. MATERIALS AND METHODS: This is a pilot, community-based, prospective, single-arm study. Physicians trained the ASHA workers regarding self-sampled HPV testing via a mobile application and telephonically using videos and e-pamphlets, who in turn trained the clients in community. The HPV kits were transported via prepaid courier service. RESULTS: Four hundred and sixty-five women of age group 30-65 years were tested by 47 teletrained ASHA workers. The mean age of ASHA worker and clients was 39.47 ± 6.45 and 37.26 ± 8.38 years, respectively. Of the ASHA workers, 91.7% were satisfied with the information provided during telecounseling, 95.7% could understand the contents of mobile app easily, and 93.6% could fill the data of clients in app easily. Of the clients, 99.6% were satisfied with counseling by ASHA workers and 98% found it easy to self-sample. The acceptability of this strategy among clients was 58.2%. The feasibility of this strategy (percentage of clients who find it easy/those who did self-sampling) was around 99%. Among those screened, 11.8% were high-risk HPV-positive and 85.5% had follow-up at the study center. CONCLUSION: The current study highlights a novel strategy of cervical cancer screening by incorporating the role of telemedicine in training ASHA workers and their role in improving the screening by home-based delivery of HPV kits with promising results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".