A cross-sectional analysis examining the association between dieting behaviours and alcohol use among secondary school students in the COMPASS study
Bibliographic record
Abstract
Background: Unhealthy weight-control methods and problematic alcohol use appear linked, with individuals engaging in both behaviours at greater risk of adverse consequences. Most studies have been conducted among females and young adults, yet both dieting and binge drinking emerge at earlier stages of development. Moreover, gender differences are likely due to contrasting body ideals. This study investigated the co-occurrence of dieting and alcohol use among youth, focusing on varying weight goals in males and females, and meal skipping, as a form of food restriction. Methods: Cross-sectional analyses were conducted in sample of 44 861 Grade 9-12 students from Year 2 (2013-14) of the COMPASS study. Results and conclusions: The majority of females were trying to lose weight, while males tended to report efforts to gain and these two groups demonstrated the highest odds of alcohol use and binge drinking. Breakfast and lunch skipping predicted binge drinking and alcohol use in females, but only the former was related to drinking behaviour in males. Breakfast skipping rarely occurred for weight loss purposes, although more females reported this reason for missing meals than males. Results support hypothesized gender variations in weight goals and meal skipping, and differing associations with drinking behaviour.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".