Changing AAPI Knowledge, Attitude, and Practice (KAP) Toward COVID-19 Pandemic in Pima County
Bibliographic record
Abstract
Background: COVID-19 had significantly impacted on the Asian American and Pacific Islander (AAPI) populations in Pima County. The Arizona and COVID-19 Update Articles (ACUA) were established to provide the latest COVID-19 information, to encourage preventive health behaviors, and to reduce the anxiety levels associated with the virus. The ACUA used the Health Belief Model as its framework. The articles addressed two of three World Health Organization dimensions of health (physical and mental). There had been 24 issues published. After two-year, an assessment was conducted to determine whether there was still a need for the articles and their impacts. Methods: This was a cross-sectional study. A KAP survey was used to collect information on knowledge gain, information usefulness, attitude/belief changes, behavior changes, and anxiety reduction. A convenience sample of AAPI leaders, community members, and health professionals was used. Results: The ACUA increased knowledge in all five areas examined. Of the five areas, more than 75 percent of the respondents indicated the knowledge gained were useful in four areas. Of the respondents, 96 percent practice preventive COVID health behaviors regularly and 80 percent were fully vaccinated for COVID and gotten their boosters. Conclusion: Three-quarter of the respondents indicated that the articles are still useful and needed. The Health Belief Model was an effective framework used in the ACUA to accomplish its health promotional goals. This health educational approach could be used in minority and underserved communities during the COVID-19 pandemic.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".