Association between Soft Drink Consumption and Aggressive Behaviour among a Quarter Million Adolescents from 64 Countries Based on the Global School-Based Student Health Survey (GSHS)
Bibliographic record
Abstract
Soft drink consumption has become a significant public health concern that is associated with various adverse health outcomes. We aim to examine the association between soft drink consumption and aggressive behavior among adolescents. We used open access data from 79 studies in 64 countries, including 263,890 adolescents aged 12-18 years who completed the global school-based student health survey (GSHS). Self-reported data on past 30-day carbonated soft drink consumption (number of times per day) and past 12-month physical fighting were utilized for analysis. Of the 263,890 participants (48% boys) aged 12-18 years, the weighted mean frequency of soft drink consumption varied from 0.5 in Kiribati to 2.5 times/day in Surname, while the weighted prevalence of frequent aggressive behavior varied from to 2.7% in Laos to 49.2% in Tuvalu. We found that each increment of soft drink consumption (time/day) was associated with an 11% (95%CI 10-13%) increase of the likelihood of frequent physical fighting. This result remained significant after adjusting for various covariates. In this large pooled sample of multinational data, there is a significant positive association between soft drink consumption and aggressive behavior among adolescents. Reducing soft drink consumption may help reduce aggressive behavior, a major risk factor for violence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".