Lower socioeconomic status, adiposity and negative health behaviours in youth: a cross-sectional observational study
Bibliographic record
Abstract
OBJECTIVE: Understanding obesity and its modifiable risk factors in youth is key to addressing the burden of cardiovascular disease later in life. Our aim was to examine the associations among adiposity, negative health behaviours and socioeconomic status in youth from the Niagara Region. DESIGN, SETTING AND PARTICIPANTS: Cross-sectional observational study of 3467 grade 9 students during their mandatory health and physical education class to investigate the association between socioeconomic status (postal code), self-reported health behaviour and adiposity in the Niagara Region, Ontario, Canada. RESULTS: Median household income was $63,696 and overall percentage below the after-tax low-income cut-off was 4.2%. Negative health behaviours (especially skipped meals, lower fruit and vegetable consumption, higher screen time) were associated with lower income neighbourhoods, however, the absolute effect was small. Those participants in the lowest income quintile had a significantly greater body mass index z-score than those in the highest (0.72±1.19 vs 0.53±1.12), but the overall trend across quintiles was not statistically significant. A similar trend was noted for waist-to-height ratio. The lowest income neighbourhoods according to after-tax low-income cut-off had small but statistically significant associations with higher adiposity compared with the middle or highest income neighbourhoods. CONCLUSIONS: Obesity prevention efforts should target modifiable behaviours, with particular attention to adolescents from lower income families and neighbourhoods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".