Exploring the disparity between public perception of COVID-19 diagnostic tests and scientific evidence: A quantitative systematic review and cross-sectional survey
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The rapid transmission of this disease has resulted in the COVID-19 pandemic. Due to global panic and misinformation, the public has questioned the validity of COVID-19 diagnostic tests in terms of their sensitivity. Our study compared the public’s familiarity to the sensitivity of quantitative reverse transcription polymerase chain reaction (RT-qPCR) and antigen tests in Canada to scientific data. A quantitative systematic review of 47 primary literature sources was conducted to determine the sensitivity of the RT-qPCR and antigen tests. Simultaneously, a survey with 105 participants was carried out to ascertain the public’s perception of these tests. The average reported sensitivity of the RT-qPCR test across the literature was 94.7%, significantly higher than that of the antigen test at 72.9% (p > 0.05). The public’s assumptions regarding the sensitivities of the RT-qPCR and antigen tests were determined to be 70-90% and 70-100%, respectively. In contrast to the findings from the quantitative systematic review, there was a significant, positive correlation (r ~ 0.5, p > 0.05) on the perceived sensitivity of the RT-qPCR versus the antigen tests for a given respondent. A negative/positive perception of one test was correlated with a negative/positive perception of the other test. Although the RT-qPCR test is reportedly more sensitive than the antigen test, the public’s perception of the sensitivity of one test is similar and correlated with the sensitivity of the other test. These results suggest the need to communicate information to the public transparently to instill trust in both tests.
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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 | 0.098 | 0.273 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".