Bibliographic record
Abstract
The purpose of this study was two-fold: (a) to identify, in a large representative sample of Canadian children, the age-related trajectories of overweight and obesity from toddlerhood into childhood and (b) to investigate the associations between these trajectories and children’s temperaments, their parents’ parenting practices and their interactions. Potentially important familial characteristics (i.e., the parents’ or surrogates’ age, income level, and educational attainment) were considered in the models. The sample for this study was drawn from the Canadian National Longitudinal Survey of Children and Youth (NLSCY). Group-based mixture modeling analyses were conducted to identify the number and types of distinct trajectories in the development of obesity (i.e., to explicate the developmental processes in the variability of childhood obesity) in a representative sample of children who were between 24 to 35 months of age, at baseline, and followed biennially over a 6-year span. Discriminant analysis was conducted to assess the theoretical notion of goodness-of-fit between parenting practices and children’s temperament, and their association with membership in the BMI trajectory groups. The results of the group-based modeling established three different BMI trajectories for the boys, namely: stable-normal BMI, transient-high BMI, and j-curve obesity. The analyses revealed four different trajectories of BMI change for the girls: stable-normal BMI, early-declining BMI, late-declining BMI, and accelerating rise to obesity. The multivariate analysis revealed that the combined predictors of the obesity trajectories of the girls (group membership) included having a fussy temperament, ineffective parenting, and parents’ educational attainment. Predictors of the boys’ obesity trajectory (group membership) included household income, parental education, and effective parenting practices. Understanding the different ways in which a child may develop obesity will allow nurses and other health professionals to take different approaches in the assessment, intervention and evaluation of obesity and obesity-related health problems. The results of this study further our understanding of factors associated with the development of obesity at a young age and hence may inform the development of early preventive programs.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".