Acute Alcohol Intoxication and Suicide Among <scp>U</scp>nited <scp>S</scp>tates Ethnic/Racial Groups: Findings from the <scp>N</scp>ational <scp>V</scp>iolent <scp>D</scp>eath <scp>R</scp>eporting <scp>S</scp>ystem
Bibliographic record
Abstract
BACKGROUND: To assess the prevalence and sociodemographic correlates of suicide involving acute alcohol intoxication among U.S. ethnic minorities. METHODS: Data were derived from the restricted 2003 to 2009 National Violent Death Reporting System. The study focused on the sociodemographic and toxicological information of 59,384 male and female suicide decedents for 16 states of the United States. Acute alcohol intoxication was defined as having a blood alcohol content (BAC) ≥0.08 g/dl. Overall, 76% of decedents were tested for the presence of alcohol. RESULTS: The proportion of suicide decedents with a positive BAC ranged from 47% among American Indians/Alaska Natives (AIs/ANs) to 23% among Asians/Pacific Islanders (PIs). Average BAC was highest among AIs/ANs. Among those who were tested for BAC, the proportion of decedents legally intoxicated prior to suicide was as follows: Blacks, 15%; AIs/ANs, 36%; Asians/PIs, 13%; and Hispanics, 28%. Bivariate associations showed that most suicide decedents who were legally intoxicated were male, younger than 30 years of age, with a high school education, not married, nonveterans, lived in metropolitan areas, and used a firearm to complete suicide. However, with the exception of Whites, most of these associations became not statistically significant in multivariate analysis. CONCLUSIONS: Alcohol use and legal intoxication prior to completing suicide are common among U.S. ethnic groups, especially among men and those who are younger than 30 years of age. The AI/AN group had the highest mean BAC, the highest rate of legal intoxication and decedents who were particularly young. Suicide prevention strategies should address alcohol use as a risk factor. Alcohol problems prevention strategies should focus on suicide as a consequence of alcohol use, especially among AI/AN youth and young adults.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".