Trajectories of beverage consumption during adolescence
Bibliographic record
Abstract
Beverages contribute substantially to daily energy and nutrient intakes. However, little is known about the co-development of beverage consumption throughout adolescence. This study aimed to investigate the presence of naturally occurring sub-groups of girls and boys following distinct trajectories of various types of beverage consumption (i.e. sugary beverages, tea and coffee, water, and milk) throughout adolescence. During the Monitoring Activities for Teenagers to Comprehend their Habits study, data were collected from 744 Canadian youths followed for six years (2013-2019). The participants were asked yearly (start-age 10-11 years old) to report how many times they consumed sugary beverages, tea and coffee, water, and milk in a week. Trajectories of beverage consumption were identified from age 11 to 18 using a person-centred approach, namely group-based multi-trajectory modelling. For girls, three different groups were identified: 'Water consumers' (62.7%), 'High beverage consumers' (20.9%), and 'Water and milk consumers' (16.4%). For boys, four different groups were identified: 'Water consumers' (39.1%), 'Water and milk consumers' (30.5%), 'Sugary drinks, coffee and tea consumers' (20.1%), and 'High beverage consumers' (10.4%). This study illustrates the complexity of beverage consumption patterns in adolescence. Various types of public health messaging and interventions may be required to promote healthier beverage consumption patterns among all adolescents.
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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 | 0.000 | 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 | 0.001 | 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".