Assessing the relationship between water and nutrition knowledge and beverage consumption habits in children
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between knowledge and beverage consumption habits among children. DESIGN: Cross-sectional analysis. Linear regression was used to identify sociodemographic, dietary and behavioural determinants of beverage consumption and knowledge, and to describe the relationships between children's knowledge and water and sugar-sweetened beverage (SSB) consumption. SETTINGS: Seventeen elementary schools in London, Ontario, Canada. PARTICIPANTS: A total of 1049 children aged 8-14 years. RESULTS: Knowledge scores were low overall. Children with higher knowledge scores consumed significantly fewer SSB (β = -0·33; 95 % CI -0·49, -0·18; P < 0·0001) and significantly more water (β = 0·34; 95 % CI 0·16, 0·52; P = 0·0002). More frequent refillable water bottle use, lower junk food consumption, lower fruit and vegetable consumption, female sex, higher parental education, two-parent households and not participating in a milk programme were associated with a higher water consumption. Male sex, higher junk food consumption, single-parent households, lower parental education, participating in a milk programme, less frequent refillable water bottle use and permission to leave school grounds at lunchtime were associated with a higher SSB consumption. Water was the most frequently consumed beverage; however, 79 % of respondents reported consuming an SSB at least once daily and 50 % reported consuming an SSB three or more times daily. CONCLUSIONS: Elementary-school children have relatively low nutrition and water knowledge and consume high proportions of SSB. Higher knowledge is associated with increased water consumption and reduced SSB consumption. Interventions to increase knowledge may be effective at improving children's beverage consumption habits.
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 | 0.003 |
| 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) | 0.002 | 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".