Maternal pre-pregnancy body mass index, child temperament, and childhood obesity risk
Bibliographic record
Abstract
Abstract Background Maternal obesity prior to and during pregnancy is related to increased risk of obesity in the child. This risk may be in part mediated by altered child temperament, which can affect mother-child interactions including feeding and soothing behaviors that affect obesity risk. Our objective was to examine the association between maternal pre-pregnancy BMI and child zBMI, and determine if child temperament, specifically positive affectivity/surgency, mediates this association. Methods Using conditional process modeling, we analyzed prospectively collected data from 408 mother-child dyads enrolled in the Alberta Pregnancy Outcomes and Nutrition (APrON) study. Child temperament was assessed by the Child Behaviour Questionnaire (CBQ) Very Short Form at 3 years of age and zBMI was calculated from in-clinic height and weight measurements at 4 years of age. Results The indirect effect of pre-pregnancy BMI on child zBMI through Surgency scores as a mediator was significant after controlling for maternal gestational weight gain, socioeconomic status, maternal anxiety and depression, and child cognitive and emotional support (β = 0.003, 95% CI [0.0001, 0.008]). Overall, maternal pre-pregnancy BMI and child zBMI were directly associated and there was an indirect association through child temperament, whereby increased Surgency was associated with higher zBMI scores. Conclusions Child zBMI score is associated with maternal pre-pregnancy BMI, and this relationship is mediated by the temperament of the child, specifically Surgency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".