P1-107 Contextual circumstances and patterns of childhood weight change
Bibliographic record
Abstract
Introduction Worldwide, the prevalence of childhood obesity has not abated, indicating that prevention strategies, traditionally implemented at the individual- level, may not be effective. Conceptualising childhood obesity within multiple levels of influence, specifically within residential communities and over the lifecourse, is necessary to design effective prevention strategies that shift the distribution of risk downward. Methods Participants of the Québec Longitudinal Study of Child Development (n=1588) comprised the sample for analysis. Standardised BMI measurements from 4 to 10 y of age and a semi-parametric mixture modelling method were used to estimate developmental trajectories of weight change. The influence of the residential environment on weight trajectories was estimated after controlling for social and early life factors, such as SES and birthweight. Results Four distinct weight trajectory groups were estimated: (1) Low- increasing (7.1%), (2) Low/medium-increasing (35.2%), (3) Medium/high- increasing (47.4%), and 4) High-stable (10.3%). Switching from urban to rural living decreased weights in Group 1, but increased weights in Group 4. In Group 2, changing from urban to medium density living increased weights. For Group 1, moving to a more cohesive neighbourhood increased weights, and moving to a more highly disordered neighbourhood decreased weights. Compared to the other three groups, Group 4 children were more likely to be overeaters and have obese mothers. Conclusion The characteristics of residential environments may play a role in childhood weight status beyond social and early life factors. These characteristics may have differing effects within the population, so a ‘one-size fits all’ strategy for intervention may not be appropriate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".