Alcohol Factors in Suicide Mortality Rates in Manitoba
Bibliographic record
Abstract
OBJECTIVE: To identify alcohol-related factors that influence mortality rates from suicide. METHOD: We examined the impact of per capita consumption of total alcohol, spirits, beer, and wine; unemployment rate; and Alcoholics Anonymous (AA) membership rate on total, male and female suicide mortality rates in Manitoba during 1976 to 1997. Time series analyses with autoregressive integrated moving average modelling were applied to total, male and female suicide rates. The analyses performed included total alcohol consumption, spirits consumption, beer consumption, and wine consumption. Missing AA membership data were interpolated with cubic splines. RESULTS: Total alcohol consumption, and consumption of beer, spirits, and wine individually, were significantly and positively related to female suicide mortality rates. Spirits and wine were positively related to total and male mortality rates. AA membership rates were negatively related to total and female suicide rates. Unemployment rates were positively related to male and total suicide rates. CONCLUSIONS: The data confirm the important relations between per capita consumption measures and suicide mortality rates. Additionally, the results for AA membership rates are consistent with the hypothesis that AA membership and alcohol abuse treatment can exert beneficial effects observable at the population level.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".