Surviving the Storm: The Impact of COVID-19 on Cervical Cancer Screening in Low- and Middle-Income Countries
Bibliographic record
Abstract
According to the Center for Disease Control and Prevention's National Breast and Cervical Cancer Early Detection Program, the cervical cancer screening rate dropped by 84% soon after the declaration of the COVID-19 pandemic. The challenges facing cervical cancer screening were largely attributed to the required in-person nature of the screening process and the measures implemented to control the spread of the virus. While the impact of the COVID-19 pandemic on cancer screening is well-documented in high-income countries, less is known about the low- and middle-income countries that bear 90% of the global burden of cervical cancer deaths. In this paper, we aim to offer a comprehensive view of the impact of COVID-19 on cervical cancer screening in LMICs. Using our study, "Prevention of Cervical Cancer in India through Self-Sampling" (PCCIS), as a case example, we present the challenges COVID-19 has exerted on patients, healthcare practitioners, and health systems, as well as potential opportunities to mitigate these challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".