Experiencing food insecurity in childhood: influences on eating habits and body weight in young adulthood
Bibliographic record
Abstract
Abstract Objective: To examine how food insecurity in childhood up to adolescence relates to eating habits and weight status in young adulthood. Design: A longitudinal study design was used to derive trajectories of household food insecurity from age 4·5 to 13 years. Multivariable linear and logistical regression analyses were performed to model associations between being at high risk of food insecurity from age 4·5 to 13 years and both dietary and weight outcomes at age 22 years. Setting: A birth cohort study conducted in the Province of Quebec, Canada. Participants: In total, 698 young adults participating in the Québec Longitudinal Study of Child Development. Results: After adjusting for sex, maternal education and immigrant status, household income and type of family, being at high risk (compared with low risk) of food insecurity in childhood up to adolescence was associated with consuming higher quantities of sugar-sweetened beverages (ßadj: 0·64; 95 % CI (0·27, 1·00)), non-whole-grain cereal products (ßadj: 0·32; 95 % CI (0·07, 0·56)) and processed meat (ßadj: 0·14; 95 % CI (0·02, 0·25)), with skipping breakfast (ORadj: 1·97; 95 % CI (1·08, 3·53)), with eating meals prepared out of home (ORadj: 3·38; 95 % CI (1·52, 9·02)), with experiencing food insecurity (ORadj: 3·03; 95 % CI (1·91, 4·76)) and with being obese (ORadj: 2·01; 95 % CI (1·12, 3·64)), once reaching young adulthood. Conclusion: Growing up in families experiencing food insecurity may negatively influence eating habits and weight status later in life. Our findings reinforce the importance of public health policies and programmes tackling poverty and food insecurity, particularly for families with young children.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".