COVID-19 Prevention Behaviors and Mistrust Among Black and Latino Public Housing Residents in NYC
Bibliographic record
Abstract
Preventive measures against COVID-19 played a crucial role in mitigating transmission. Social and structural factors influence individuals’ trust in health care and engagement in health-promoting behaviors. This study investigates racial-ethnic differences in COVID-19 prevention behaviors and beliefs among public housing residents in the South Bronx, NYC. Data come from the Nurse-Community-Family Partnership study, a randomized controlled trial conducted during the COVID-19 pandemic. The analytic sample ( n = 200) was limited to adult participants who identified as non-Hispanic Black, Hispanic Black, Hispanic White, or Hispanic Other. Multilevel logistic regression models estimated odds ratios and 95% confidence intervals, adjusted for sex, age, and education. The odds of receiving a COVID-19 vaccine were 3.8 times greater for Hispanic White participants and 2.5 times greater for Hispanic Other participants than for non-Hispanic Black participants. In addition, the odds of practicing social distancing were 2.2 times greater for Hispanic Other participants than for non-Hispanic Black counterparts. COVID-19-related government mistrust was associated with an 88% decrease in the odds of vaccinating, a 58% decrease in the odds of practicing social distancing, and a 77% decrease in the odds of mask-wearing. COVID-19 vaccine mistrust was associated with a 93% decrease in the odds of vaccination. When adjusted for mistrust, differences in vaccination rates by racial-ethnic groups were no longer significant. Addressing mistrust is pivotal for improving public health outcomes. Interventions that enhance trust in health institutions through cultural competence, community engagement, and greater representation in health care can help bridge the gap in prevention behaviors among racially minoritized groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".