A qualitative descriptive study exploring perspectives on a school-based take-home COVID-19 saliva testing program
Bibliographic record
Abstract
Objectives: Saliva testing, a safe and comfortable screening modality, can effectively detect SARS-CoV-2 in children. Recognizing the need for accessible testing in schools, a COVID-19 School Testing Program was launched across 677 schools, providing take-home saliva kits, educational materials, and ongoing support to families and school leadership. This study aimed to explore participants' experiences with the program to inform future school-based public health initiatives. Methods: Using a prospective qualitative descriptive design, we conducted semi-structured interviews until thematic saturation was reached. Interviews were audio recorded, transcribed, and analyzed through inductive content analysis, with reflexivity, use of a critical friend, and thick description ensuring methodological rigor. Twenty-one participants were interviewed. Results: Six themes emerged: (1) improved access, (2) flexibility in testing environment, (3) less invasive option, (4) convenient drop-off, (5) prompt results, and (6) enhanced school safety. Conclusion: Findings emphasize accessibility, convenience, and flexibility as essential for effective school-based viral testing models.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".