Obesity-related Behaviors of US- and Non-US-born Parents and Children in Low-income Households
Bibliographic record
Abstract
OBJECTIVE: To examine differences in obesity-related behaviors by parental US-born status among low-income, minority families participating in Healthy Habits, Happy Homes, an intervention trial to improve household routines for childhood obesity prevention. Evidence suggests lower obesity risk among adult immigrants, but research is inconclusive regarding the influence of having a non-US-born parent on childhood obesity. METHOD: We sampled 57 US-born and 64 non-US-born families of children aged 2 to 5.9 years living in the Boston area. At baseline, parents reported their own screen time, physical activity, diet, and sleep as well as their children's behaviors. We used linear and logistic regression to examine the association of parental US-born status with obesity-related behaviors. RESULTS: Mean (SD) body mass index z score was 0.94 (1.16), and it did not differ between the groups. After adjusting for parental education and child race/ethnicity, children of non-US-born (vs US-born) parents had later bedtimes (0.81 hours later; 95% confidence interval [CI], 0.37-1.25) and wake-up times (0.56 hours later; 95% CI, 0.16-0.95) and engaged in less active play (0.15 fewer hr/d; 95% CI, -0.28 to -0.01). Non-US-born (vs US-born) parents had less screen exposure. CONCLUSION: In this cross-section of low-income, urban families, having a parent born outside the United States was associated with a profile of risk and protective behavior; adjustment for education and race/ethnicity removed the protective associations of parental nativity with child behavior. Obesity-related differences in behaviors and home environments should be considered when designing interventions targeting low-income communities with a high proportion of non-US-born participants.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".