COVID-19 vaccination access, acceptability, and pandemic recovery in American Indian communities.
Bibliographic record
Abstract
OBJECTIVE: The present study explored perspectives on COVID-19 vaccine acceptability, access, and strategies for pandemic recovery among rural and urban/suburban American Indian (AI) community leaders and members in California. METHOD: The qualitative study was initiated by a community-academic partnership with a large AI health organization and two universities and included virtual focus groups focused on COVID-19 vaccine acceptability (concerns, risks, benefits), initial vaccine rollout accessibility (vaccination site preferences, accessibility, strategies for improving vaccination), and recommendations for pandemic recovery. Reflexive thematic analysis was used to generate themes. RESULTS: = 12). Participants in both urban/suburban and rural settings reported preferences for Tribal or Indian Health Service clinics for vaccination and recommended culturally tailored COVID-19 educational materials, health services, and community events to promote pandemic recovery. Participants in rural groups provided examples of tailored community-led pandemic care but illustrated how health care access limited vaccination, how basic needs affected vaccine prioritization, and how gaps in data on AI communities limited local informed decision-making. CONCLUSION: Findings demonstrate differences in the COVID-19 experience among AI adults living in urban/suburban and rural regions, including vaccine access and basic needs concerns. Findings also highlight local preferences in the pandemic community response and recommendations for culturally tailored health information, health services, and gatherings. Public health campaigns may require additional resources for AI communities to improve equitable distribution and uptake. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".