Mental Health Challenges In Caring For American Indians and Alaska Natives
Bibliographic record
Abstract
American Indians (AI) and Alaska Natives (AN), descendants of the indigenous people, are a diverse population group growing in number every year. Studies have shown that American Indians and Alaska Natives (AI/ANs) have a decreased life expectancy, higher mortality rate, and lower quality of life than the general US population.In addition to having higher rates of general medical conditions such as diabetes, obesity, and high blood pressure, there is a high prevalence of mental health problems and psychiatric comorbidity amongst American Indians and Alaska Natives (AI/ANs). A national study comparing the prevalence of mental health disorders and associated treatment-seeking results showed higher rates of psychiatric disorders in American Indians and Alaska natives than non-Hispanic whites. Post-traumatic stress disorder (PTSD), violence, suicide, and substance use have been identified as some of the more prevalent mental health issues among AI/ANs when compared with the general population in the United States. Sociodemographic characteristics, including age, education, and income, are likely contributing factors for the number of psychiatric disorders seen in American Indians and Alaska Natives than other racial groups. There should be an increased effort to improve AI/AN mental health care disparities through culturally competent clinical interventions. In working towards this goal, it is important to identify the existing disparities in mental health care delivery and outcomes among AI/ANs. This will then help guide the steps that are necessary for improved outcomes and reduction in health disparities.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".