Assessing COVID-19–Related Knowledge, Attitudes, and Practices Among Hispanic Primary Care Patients: Protocol for a Cross-sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Recent epidemiological data indicate that minority groups, especially Hispanic communities, experience higher rates of infection, hospitalization, and death due to COVID-19. It is important to understand the nature of this health disparity and the socioeconomic or behavioral factors that are placing Hispanic communities and other minority populations at higher risk for morbidity and mortality. OBJECTIVE: The purpose of this project is to assess current COVID-19-related knowledge, attitudes, and practices (KAP) among a predominantly Hispanic population from Orange County, California, and identify risk factors that may contribute to increased susceptibility and vulnerability to contracting SARS-CoV-2. METHODS: Our Orange County-wide community survey consists of quantitative survey questions in four domains: demographic information, COVID-19 knowledge questions, COVID-19 attitude questions, and COVID-19 practices questions. The survey questions are adapted from recent global KAP studies. Participants are being recruited from Amistad Medical Clinic, a private primary health clinic group in Orange County that treats a predominantly Hispanic population. Patients recruited during telehealth visits are surveyed remotely by telephone, and those recruited during in-person clinic visits are surveyed in person. Surveys are conducted by trained members of the study team who are native to the community setting. RESULTS: As of October 12, 2020, we had recruited and enrolled 327 participants. Data collection occurred June 26th to October 30th. Data analysis is ongoing. CONCLUSIONS: Very few current COVID-19 studies focus on the perspective and experience of minority populations. Because Hispanic communities are disproportionately affected by COVID-19, it is important to understand the factors the contribute to this disparity and the next steps that should be taken to reduce the COVID-19 burden in this population. We believe that our study model of partnering with a local clinic system that serves our study population can be expanded to other settings to compare COVID-19 KAP and associated factors within different minority communities. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/25265.
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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 | 0.022 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".