Identifying Success Factors for Optimizing COVID-19 Vaccine Uptake Among Indigenous Populations in Taiwan: Cross-Sectional Questionnaire Survey
Bibliographic record
Abstract
Background: The COVID-19 pandemic has devastated economies and strained health care systems worldwide. Vaccination is crucial for outbreak control, but disparities persist between and within countries. In Taiwan, certain indigenous regions show lower vaccination rates, prompting comprehensive inquiries. Objective: This study aims to identify predictors for COVID-19 vaccination and develop strategies for indigenous communities. Methods: This cross-sectional study, conducted from May 13 to July 18, 2022, surveyed indigenous community members older than 55 years residing in a mountain area in southern Taiwan. Based on the health belief model, the questionnaire covered sociodemographic factors, health-related issues, and trust in physicians. The analysis included bivariate analysis, logistic regression, and mediation analysis. Results: Most participants (N=203) were aged 55-64 years (102/203, 50.2%), female (129/203, 63.5%), married (104/203, 51.2%), with low education (165/203, 81.3%), and engaged in agriculture (79/203, 38.9%) or were unemployed (104/203, 51.2%). Logistic regression revealed that unvaccinated individuals were significantly more likely to perceive lower COVID-19 threats (P=.03), fewer vaccination benefits (P=.04), higher barriers to vaccination (P=.02), and weaker responses to external cues to action (P<.001), while no significant differences were observed in trust in physicians. Mediation analyses further indicated that trust in physicians influenced vaccine uptake indirectly through perceived barriers. The indirect effect was statistically significant (95% bootstrap CI 0.013 to 0.437), suggesting a full mediation effect. Conclusions: Effective pandemic prevention strategies for indigenous communities should be grounded in a nuanced understanding of local needs and incorporate bottom-up approaches to avoid cultural saturation and the exacerbation of existing health disparities. Ensuring the accuracy and clarity of vaccine-related information received by indigenous older adults is essential. Local health authorities should consider deploying health care professionals to engage directly with indigenous older adults and their caregivers, delivering culturally appropriate and evidence-based information to address concerns regarding vaccine safety and perceived risks. Such efforts are critical to strengthening vaccine confidence and increasing vaccination uptake in these communities.
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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".