Rapid SARS-CoV-2 Antigen Detection Self-Tests to Increase COVID-19 Case Detection in Peru: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic heavily impacted many low- and middle-income countries (LMICs), such as Peru, overwhelming their health systems. Rapid antigen detection self-tests for SARS-CoV-2, the virus that causes COVID-19, have been proposed as a portable, safe, affordable, and easy-to-perform approach to improve early detection and surveillance of SARS-CoV-2 in resource-constrained populations where there are gaps in access to health care. OBJECTIVE: This study aims to explore decision makers' values and attitudes around SARS-CoV-2 self-testing. METHODS: In 2021, we conducted a qualitative study in 2 areas of Peru (urban Lima and rural Valle del Mantaro). Purposive sampling was used to identify representatives of civil society groups (RSCs), health care workers (HCWs), and potential implementers (PIs) to act as informants whose voices would provide a proxy for the public's attitudes around self-testing. RESULTS: In total, 30 informants participated in individual, semistructured interviews (SSIs) and 29 informants participated in 5 focus group discussions (FGDs). Self-tests were considered to represent an approach to increase access to testing that both the rural and urban public in Peru would accept. Results showed that the public would prefer saliva-based self-tests and would prefer to access them in their community pharmacies. In addition, information about how to perform a self-test should be clear for each population subgroup in Peru. The tests should be of high quality and low cost. Health-informed communication strategies must also accompany any introduction of self-testing. CONCLUSIONS: In Peru, decision makers consider that the public would be willing to accept SARS-CoV-2 self-tests if they are accurate, safe to use, easily available, and affordable. Adequate information about the self-tests' features and instructions, as well as about postuse access to counseling and care, must be made available through the Ministry of Health in Peru.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".