Poverty's latent effect on adiposity during childhood: evidence from a Québec birth cohort
Bibliographic record
Abstract
BACKGROUND: Childhood poverty heightens the risk of obesity in adulthood, but the age at which this risk appears is unclear. We analysed the association between poverty trajectories with body mass index (BMI) Z-scores or the risk of being overweight or obese across four ages (6 years, 8 years, 10 years and 12 years) in childhood. METHODS: Data were from the 1998-2010 'Quebec Longitudinal Study of Child Development' cohort (n=698). Poverty was defined using Statistics Canada's thresholds, and trajectories were characterised with a Latent Class Growth Analysis. Multivariable linear and logistic regression models adjusted for sex, whether the mother was an immigrant, maternal education and birth weight. RESULTS: Four income trajectories were identified: a reference group (stable non-poor), and 3 higher exposure categories (increasing likelihood of poverty, decreasing likelihood of poverty or stable poor). Compared with children from stable non-poor households, children from stable poor households had BMI Z-scores that were 0.39 and 0.43 larger than children from stable non-poor households at age 10 years and 12 years, respectively (p<0.05). Compared with children from stable non-poor households, children from stable poor households were 2.22, 2.34, and 3.04 times more likely to be overweight or obese at age 8 years, 10 years and 12 years, respectively (p<0.05). CONCLUSION: A latency period for the detrimental effects of child poverty on the risk of overweight or obesity was detected. Whether the effects continue to widen with increasing duration of exposure to poverty as the children age should be investigated.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".