Knowledge, Attitude, Practices, and Vaccine Hesitancy Among the Latinx Community in Southern California Early in the COVID-19 Pandemic: Cross-sectional Survey
Bibliographic record
Abstract
Background The Latinx population in the United States has experienced high rates of infection, hospitalization, and death since the beginning of the COVID-19 pandemic. There is little data on the knowledge, attitude, and practices (KAP) specifically in Latinx communities in the United States. Objective We aimed to assess COVID-19 KAP and vaccine hesitancy among a Latinx cohort in the early stages of the COVID-19 pandemic (from July 2020 to October 2020), at a unique time when a vaccine was not available. Methods Participants aged ≥18 years were recruited at a primary care clinic in Southern California and asked to self-report sociodemographic characteristics, KAP, and vaccine hesitancy. A subset of the participants answered the vaccine hesitancy assessment as it was added after the start of data collection. KAP items were summed to create composite scores, with higher scores reflecting increased COVID-19 knowledge, positive attitudes toward the COVID-19 pandemic, and disease prevention practices. Bivariate and multivariable regression models were fitted to test associations between sociodemographic characteristics and KAP scores. For our analysis, we only included patients who self-identified as Latinx. Results Our final data set included 265 participants. The participants had a mean age of 49 (IQR 38.5-59) years, and 72.1% (n=191) were female, 77% (n=204) had at most a high school degree, 34.7% (n=92) had an annual income Conclusions Good knowledge and preventative practices in the population may have reflected effective public health messaging and the implementation of public health laws during the first wave of the pandemic; however, the overall fear and anxiety may have reflected the negative impact that the pandemic had on vulnerable populations such as the Latinx community. Although our data are a reflection of a previous time in the pandemic, we believe it captures a critical time that can be used to provide unique insights regarding potential avenues to better protect the Latinx communities against future vaccine-resistant COVID-19 strains. International Registered Report Identifier (IRRID) RR2-10.2196/25265
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".