COVID-19 Prevalence, Risk Perceptions, and Preventive Behavior in Asymptomatic Latino Population: A Cross-Sectional Study
Bibliographic record
Abstract
Aims To determine the prevalence, level of coronavirus disease 2019 (COVID-19) risk perception attitude and preventive behavior implemented by the Latino population in the United States of America (USA). Methods This cross-sectional study was conducted between July 25 and August 25, 2020, and included asymptomatic Latino participants (n=410) with no current/previous COVID-19 within a religious community in Maryland. Participants answered a questionnaire consisting of three components: patient demographics/socioeconomic status, COVID-19 risk perception, and precautionary behavior. Additionally, a focused history taking and physical examination were performed, and nasal swabs for COVID-19 testing were obtained. Results Around 80% of our study population was 35 years and older, considerably healthy, with only about a third reporting history of chronic disease (~80%); most were females (~66%). Of our participants, 90% lived under poverty; only ~6% had made it to college. When asked about the likelihood of acquiring COVID-19, 97.3% stated they have a low risk of getting infected. However, as we asked about the risk of individuals living in their neighborhood, state, and country, the rates changed to moderate to high (78.4%, 86.3%, and 86.6%, respectively). When asked about preventive behavior, 71.2% stated they never wear masks and 85.4% mentioned they never keep social distance. Additionally, 76 (18.5%) tested positive for COVID-19, whereas 64 (84.2%) developed symptoms at follow-up, 57 (75%) were hospitalized, and 4 (5.2%) died. Conclusions Our study identified inadequate COVID-19 threat perception and lack of engagement in preventive behavior among a group of Latinos living in the USA. We believe that Latino communities across the USA are at markedly high risk of acquiring, spreading, and dying of COVID-19.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".