Out of Adversity Comes Opportunity: Smart-Colpo National Program for the Elimination of Carcinoma Cervix in a Post–COVID-19 World
Bibliographic record
Abstract
Background Carcinoma cervix is one of the leading causes of death among women worldwide. The World Health Organization has put forth the 90-70-90 global strategy for the elimination of cervical cancer as a public health problem. It calls for 70% women to be screened at least once in their lifetime. However, this rate is as low as 1.9% for India and even lower for many other countries, making the target insurmountable, especially in resource-constrained settings. The COVID-19 pandemic made this even more challenging. Objective This study aimed to identify bottlenecks and high leverage points and propose a technology-driven, national-level program for improving the screening of carcinoma cervix. Methods Detailed process mapping was done to identify potential bottlenecks. A counterfactual approach was used to identify high leverage points for impact using “What if” scenarios. These findings were used to build program theory–based logic models to propose a national-level program for carcinoma cervix prevention. Results Availability, accessibility, affordability, skewed distribution of infrastructure, cost implications, and limited specialist workforce were identified as bottlenecks. The COVID-19 pandemic put a strain on existing resources and worsened the situation. The existing network of primary health care workers, changes in health-seeking behavior due to COVID-19 with the increasing role of tele-health, conducive political milieu with initiatives such as Digital India Mission, and a booming start-up ecosystem were identified as high leverage points through the counterfactual approach. Using these findings, a national program was designed with program theory–based logic modeling. Conclusions The journey from <2% of women screened at least once in their lifetime all the way to 70% would need a drastic increase in funding and resource allocation, which is unlikely considering the current conditions. COVID-19 has not only been an adversity but also opened new thinking and opportunities. Artificial intelligence–driven, cost-effective, easy-to-use, and widely acceptable solutions such as “Smart-Colpo” can be a game changer to achieve the World Health Organization targets. Conflicts of Interest None declared.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".