Correlates of sugar-sweetened beverages consumption among adolescents
Bibliographic record
Abstract
OBJECTIVE: To identify correlates and underlying beliefs regarding the adolescents' intention to abstain from consuming sugar-sweetened beverages (SSB) and the consumption of ≤1 daily portion of SSB. DESIGN: Correlational study. SETTING: Region of Chaudière-Appalaches in the province of Quebec, Canada. PARTICIPANTS: 311 adolescents aged 13-18 years completed a self-administrated online questionnaire based on the Reasoned Action Approach. Frequency and quantity of different types of SSB within the past month were measured. RESULTS: Total mean SSB intake was 882·6 ml/d (654·0 kJ/d ). Only 11·3 % abstained from SSB within the last month. Intention to abstain from SSB was explained by identification as SSB abstainers (β = 0·47), perceived norm (β = 0·32), attitude (β = 0·30), age 13-14 years (β = -0·27) and perception of the school environment (β = 0·14), which explained 66 % of the variance. Consumption of ≤1 daily portion of SSB was explained by the intention to abstain (OR = 1·55; 95 % CI 1·14, 2·11), perceived behavioural control to abstain (OR = 1·80; 95 % CI 1·29, 2·52), sex (girls v. boys: OR = 2·34; 95 % CI 1·37, 3·98) and socio-economic status (advantaged v. disadvantaged school: OR = 2·08; 95 % CI 1·21, 3·56). Underlying beliefs (i.e. more energy, decreased risk of addiction and friends' approval) associated with intention as well as perceived barriers (e.g. access to SSB, after an activity that makes you thirsty), and facilitating factors (e.g. access to water) linked to SSB consumption were identified. CONCLUSIONS: The results can inform public health interventions to decrease SSB consumption and their associated health problems among 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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".