Factors related to heavy drinking between British Columbia Asian adolescents and South Korean adolescents
Bibliographic record
Abstract
Abstract Purpose The aim of this study is to compare the factors related to heavy drinking among British Columbia (BC) Asian adolescents and South Korean adolescents. Design and Methods A cross‐sectional descriptive design was used. Participants were 72,422 adolescents (12,382 BC Asian adolescents and 60,040 South Korean adolescents) from the 2018 BC Adolescent Health Survey and the 2018 Korean Youth Risk Behavior Web‐Based Survey. Complex samples descriptive statistics, Rao–Scott χ 2 tests, and complex samples logistic regression analyses were performed. Results Heavy drinking was reported by 8.6% of BC Asian adolescents and 7.7% of South Korean adolescents. Asian adolescents in BC and South Korea shared six risk factors and one protective factor linked to odds of heavy drinking. The strongest risk factor for heavy drinking in each region was current cigarette smoking. Other risk factors for heavy drinking included older age/higher grade (10/12th), early initiation of sexual intercourse (age 14 or younger), experiences of bullying, depression, and exercise. The only protective factor for heavy drinking, sufficient sleep, was similar in both regions. Practice Implications This study suggests several nursing interventions and health promotion strategies to help us to prevent or reduce heavy drinking for BC Asian adolescents and South Korean adolescents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".