Non-pharmacotherapeutic Management of Alcohol Use Disorder in the Alaska Native Population: A Narrative Review
Bibliographic record
Abstract
Alcohol use disorder (AUD) is a leading preventable cause of death in the United States and has had a greater health impact on Alaska Natives than on any other racial group. To date, AUD in these communities has had wide-reaching negative impacts contributing to high rates of suicide, homicide, and accidents. A variety of genetic, experiential, social, and cultural factors have been associated with this trend. For decades, the Alaska Native subgroup has received inadequate treatment. The purpose of this review is to evaluate current trends in effective interventions and to help answer the question: What may comprise a successful non-pharmacotherapeutic interventional strategy to treat and prevent AUD in Alaska Natives? A database literature search was performed in September 2022 using the PubMed library. Search terms included (alcohol use disorder) AND ((Alaska OR Alaskan) Native). Inclusion criteria included full-text articles, a focus on specific non-pharmacotherapeutic treatment strategies, and a publication date after 2005. Studies that did not evaluate non-pharmacotherapeutic interventions, evaluated a population other than Alaska Natives, evaluated a disorder other than AUD, were written in a language other than English, or were editorials or opinion pieces were excluded. The selected studies were assessed for bias utilizing the Newcastle-Ottawa Scale (NOS). Twelve studies were included in this review. This review found that early social network intervention, incentive-driven programs, culturally-driven programs, and motivational interviewing are promising non-pharmacotherapeutic interventions in the treatment of AUD in Alaska Native communities. Evidence suggests that a shift in focus to the accentuation of protective factors and the mitigation of isolation as a risk factor, rather than on the reduction of more intractable risk factors, may be associated with improved outcomes in treating AUD. The literature also suggests that successful prevention strategies should be driven by indigenous knowledge and grounded in community and culture. This study has its limitations. These include a lack of direct comparisons between studies, a lack of pooled statistical analysis or synthesis, and a lack of quantitative analysis. Instead, the majority of data is gathered from more bias-prone cross-sectional studies and, thus, should be used to provide insight into potential risk factors and non-pharmacologic therapies effective in this population rather than as strong evidence in favor of one therapeutic regimen over another. For this, there is a need for more clinical trials evaluating treatments for AUD in this population. This review received support from the University of South Florida Department of Psychiatry. There were no sources of funding for this work from any institution. There are no competing financial or non-financial interests that may be interested in this work. This review is not registered. This review does not have a prepared protocol.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".