The nexus of food and nutrition security in youth: linking a Canadian survey and administrative data
Bibliographic record
Abstract
Abstract Background Food insecurity is impacted by social determinants of health and is a risk factor for poor nutrition. Simultaneously, nutrition insecurity remains a pervasive public health concern in wealthy countries, especially for children. This study describes characteristics of youth that may increase risk of food and nutrition insecurity. Methods A cross-sectional study of grade nine students took place in 37 schools in Manitoba, Canada. Students completed a web-based survey on food security, diet, and nutrition-related behaviours. When consenting, parents/guardians could provide their child's health number. This sub-sample (n = 943) was linked with social and health variables through a de-identified data repository and used for this study. Bivariate associations explored relationships between sociodemographic information, diet quality, eating behaviours, mental health, and food security. A multivariable logistic regression model determined adjusted odds ratios and 95% confidence limits for food security, and a general linear model for diet quality. Significance was set at p < 0.05 for all analyses. Results Twenty percent of participants were food insecure and 70% had sub-optimal diets. Food security was positively associated with living in a rural area, in higher income neighbourhoods and eating family dinner more frequently, while living in a northern area or in social housing, were negatively associated. Food security was not significantly related to diet quality; however, better nutrition was associated with being female, higher self-rated health, absence of a mental health diagnosis, and more frequent breakfast consumption, while receiving social assistance and living in a northern area were negatively associated with diet. Conclusions While results show a socioeconomic and geographic vulnerability to food insecurity, the diet quality of most adolescents was low. A multidimensional public health approach is needed to resolve these connected yet different issues. Key messages • Food insecurity and nutrition insecurity are public health concerns that require distinct but related interventions. • Scaling up income supports to resolve food insecurity is insufficient to improve dietary quality across adolescent populations.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.022 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".