Exploring sex-specific time trends in drinking patterns in the Greenlandic population from 1993 to 2014 – a large Arctic Indigenous population
Bibliographic record
Abstract
A drinking pattern characterised by occasional excessive drinking is a key challenge for public health in Greenland. The objective was to examine sex-specific time trends in drinking patterns among Greenland Inuit. Cross-sectional and cohort data from geographically representative health surveys in 1993, 2005-2010 and 2014 were included (n = 4,938). Drinking patterns were defined as abstainer, non-problematic and occasional binge drinking. Patterns were analysed by sex-specific crude proportions and logistical analyses according to age, birth cohort and calendar time, accounting for region and settlement type. More than half of the men and one-third of the women had an occasional binge drinking pattern, while 22.6% of females and 15.1% of men were abstainers. Abstention increased with increasing age, while occasional binge drinking decreased among men. Younger male birth cohorts were less likely to have an occasional binge drinking pattern, while the youngest females had the highest odds ratio. A drinking pattern characterised by occasional excessive use remains a key challenge for public health in Greenland with age as a strong predictor. A high prevalence of abstainers co-exists with a high prevalence of occasional binge drinking. The increased odds ratio for occasional binge drinking among younger females should be addressed further.
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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 | 0.001 |
| 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".