The direct and indirect relationships between alcohol prevention measures and alcoholic liver cirrhosis mortality.
Bibliographic record
Abstract
OBJECTIVE: The objective of this article is to investigate direct and indirect relationships between prevention measures and alcoholic liver cirrhosis mortality in Canadian provinces from 1968 to 1986. METHOD: The data base that was assembled included alcoholic cirrhosis mortality rates, alcohol availability measures (rate of licensed premises, year in which the legal drinking age was reduced), per capita consumption of alcohol, rates of AA members and groups, and economic and demographic measures. This article develops a two-equation analytic model based on the availability theory of alcohol problems and prevention (Single, 1988). The distinction between direct and indirect effects of prevention measures can be made explicitly with this model. RESULTS: Alcohol availability measures, but not AA measures, had a significant direct potential impact on alcohol consumption. AA measures had a significant direct relationship to cirrhosis mortality rates. Alcohol consumption also had a significant direct relationship to cirrhosis mortality, and alcohol availability measures had an important indirect relationship through their influence on per capita alcohol consumption. CONCLUSIONS: While these observations need to be interpreted cautiously, the two-equation model shows promise as an approach to understanding direct and indirect influences on alcohol problems. As expected, AA measures and per capita alcohol consumption demonstrated significant direct relationship to cirrhosis mortality. In addition, important indirect influences of drinking-age changes and rates of licensed premises on cirrhosis mortality were observed through their relationships to per capita alcohol consumption.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".