Research Article Underage Binge Drinking Adolescents: Sociodemographic Profile and Utilization of Family Doctors
Bibliographic record
Abstract
Copyright © 2013 Esme Fuller-Thomson et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Context. Binge drinking (more than five drinks on one occasion) is a major public health problem among teenagers in the US, Canada, and Europe. Negative outcomes to binge drinking include alcohol related injuries and accidental death. Family physicians are the main point of contact between binging adolescents and the health care system. Design and Setting. This study was based on a secondary analysis of 6,607 respondents aged 15–17 from the regionally representative data acquired through the Canadian Community Health Survey 1.1. Results. According to our findings, one in every eight teens aged 15–17 binge drank monthly. The odds of binge drinking were higher among males, Whites, those living away from parents, teens who reported a decline in health status, and those experiencing back problems and depression. Smoking status was strongly associated with the binge drinking behavior.Three-quarters of binge drinking adolescents had seen their family doctor in the past year but only one in ten had spoken with any health professional about a mental health issue. Conclusions. Family physicians need to screen their adolescent patients for binge drinking in order to provide timely and effective interventions. Awareness of the profile of binge drinkers could improve the accuracy of targeting and outreaching strategies. 1.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".