Alcohol Consumption, Alcoholics Anonymous Membership, and Homicide Mortality Rates in Ontario 1968 to 1991
Bibliographic record
Abstract
BACKGROUND: Research has shown a strong link between alcohol use and a variety of problems, including violence. Parker and colleagues have presented a selective disinhibition theory for the link between alcohol use and homicide (and other violence) that posits a causal relationship that is also influenced by other situational and contextual factors. This model is particularly well suited for aggregate-level investigations. In this study, we examine the impact of alcohol factors, including consumption measures and Alcoholics Anonymous (AA) membership rates, on homicide mortality rates in Ontario, and test predictions derived from the selective disinhibition model. METHODS: Time series analyses with ARIMA modeling were applied to total, male, and female homicide rates in Ontario between 1968 and 1991. The analyses performed included total alcohol consumption, spirits consumption, beer consumption, and wine consumption. Missing AA membership data were interpolated with cubic splines. RESULTS: For the total population and males, homicide rates were significantly and positively related to total alcohol consumption and to the consumption of beer and spirits. They were also negatively related to AA membership rates in the analyses involving spirits and wine and positively related to unemployment rates in the analyses involving beer, wine, and total alcohol. Among females, none of the measures were significant predictors of homicide mortality rates. CONCLUSIONS: These data provide important support for the selective disinhibition model and confirm important relationships between per capita consumption measures and homicide mortality rates, especially among males, seen in other studies. Additionally, the results for AA membership rates are consistent with the hypothesis that AA membership and treatment for misuse of alcohol 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 | 0.001 |
| 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".