COVID-19 Sero-Prevalence and Risk Factors in a Sample of Community Health Center Employees in New York State
Bibliographic record
Abstract
OBJECTIVE: To document COVID-19 sero-prevalence, prior testing, symptom experiences, and risk factors in a sample of community health center (CHC) workers. METHODS: Descriptive statistics and log-binomial regression were used to analyze an electronic employee survey linked with COVID-19 antibody results. The sample included 378 employees who completed the survey; 325 had complete lab data. RESULTS: The sero-positivity rate was 15.4%. One third of sero-positive participants had no previous COVID-19 symptoms or were unsure. Working on-site only and/or with direct patient contact was not associated with sero-positivity. Employees in their 20s were more likely to test positive than employees ages 50+, controlling for sex, race, and region (PR = 2.96; P < 0.05). CONCLUSIONS: With CHCs central to COVID-19 response and vaccination efforts, public health messaging should remind CHC workers, especially younger employees, of their risks of community-based exposure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".