Nasopharyngeal swabs vs. saliva sampling for SARS-CoV-2 detection: A cross-sectional survey of acceptability for caregivers and children after experiencing both methods
Bibliographic record
Abstract
Background: Saliva sampling is a promising alternative to nasopharyngeal swabs for SARS-CoV-2 testing, but acceptability data is lacking. We characterize the acceptability of saliva sampling and nasopharyngeal swabs for primary decision makers and their children after experiencing both testing modalities. Results: 48 participants and 48 primary decision makers completed the survey. Nasopharyngeal swab acceptability differed between scenarios, ranging 79% [95%CI: 66, 88] to 100% [95%CI: 95, 100]); saliva sampling acceptability was similar across scenarios, ranging 92% [95%CI: 82, 97]) to 98% [95%CI: 89, 99]. 58% of youth described significant pain with nasopharyngeal swabbing, versus none with saliva sampling. 90% of children prefer saliva sampling. 66% of primary decision makers would prefer nasopharyngeal swabbing if it were 10% more sensitive. Conclusion: Though youth prefer saliva sampling over nasopharyngeal swabs, primary decision makers present for testing remain highly accepting of both. Acceptance of nasopharyngeal swabs, however, varies with the testing indication and is influenced by perceived test accuracy. Understanding factors that influence sampling acceptance will inform more successful testing strategies.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".