The Cultural Adaption of a Sobriety Support App for Alaska Native and American Indian People: Qualitative Feasibility and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: Despite high rates of alcohol abstinence, Alaska Native and American Indian (ANAI) people experience a disproportionate burden of alcohol-related morbidity and mortality. Multiple barriers to treatment exist for this population, including a lack of culturally relevant resources; limited access to or delays in receiving treatment; and privacy concerns. Many ANAI people in the state of Alaska, United States, live in sparsely populated rural areas, where treatment access and privacy concerns regarding peer-support programs may be particularly challenging. In addition, prior research demonstrates that many ANAI people prefer a self-management approach to sobriety, rather than formal treatment. Taken together, these factors suggest a potential role for a culturally adapted smartphone app to support ANAI people interested in changing their behavior regarding alcohol use. OBJECTIVE: This study was the first phase of a feasibility and acceptability study of a culturally tailored version of an off-the-shelf smartphone app to aid ANAI people in managing or reducing their use of alcohol. The aim of this qualitative needs assessment was to gather insights and preferences from ANAI people and health care providers serving ANAI people to guide feature development, content selection, and cultural adaptation before a pilot test of the smartphone app with ANAI people. METHODS: From October 2018 to September 2019, we conducted semistructured interviews with 24 ANAI patients aged ≥21 years and 8 providers in a tribal health care organization in south-central Alaska. RESULTS: Participants generally endorsed the usefulness of a smartphone app for alcohol self-management. They cited anonymity, 24/7 access, peer support, and patient choice as key attributes of an app. The desired cultural adaptations included ANAI- and land-themed design elements, cultural content (eg, stories from elders), and spiritual resources. Participants considered an app especially useful for rural-dwelling ANAI people, as well as those who lack timely access to treatment services or prefer to work toward managing their alcohol use outside the clinical setting. CONCLUSIONS: This needs assessment identified key features, content, and cultural adaptations that are being implemented in the next phase of the study. In future work, we will determine the extent to which these changes can be accommodated in a commercially available app, the feasibility of implementation, and the acceptability of the culturally adapted version of the app among ANAI users.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".