Impact of Maternal Health Behaviours and Social Conditions on Infant Diet at Age 1-Year: Results from a Prospective Indigenous Birth Cohort in Ontario, Canada
Bibliographic record
Abstract
Background: Understanding the impact of maternal health behaviours and social conditions on childhood nutrition is important to inform strategies to promote health during childhood. Objective: To describe how maternal health sociodemographic factors (e.g., socioeconomic status, education), health behaviours (e.g., diet), and traditional health care use during pregnancy impact infant diet at age 1-year. Methods: Data were collected from the Indigenous Birth Cohort (ABC) study, a prospective birth cohort formed in partnership with an Indigenous community-based Birthing Centre in southwestern Ontario, Canada. 110 mother-infant dyads are included in the study and were enrolled between 2012 and 2017. Multiple linear regression analyses were performed to understand factors associated with infant diet scores at age 1-year, with a higher score indicating a diet with more healthy foods. Results: The mean age of women enrolled during pregnancy was 27.3 (5.9) years. Eighty percent of mothers had low or moderate social disadvantage, 47.3% completed more than high school education, and 70% were cared for by a midwife during their pregnancy. The pre-pregnancy body mass index (BMI) was <25 in 34.5% of women, 15.5% of mothers smoked during pregnancy, and 14.5% of mothers had gestational diabetes. Being cared for by an Indigenous midwife was associated with a 0.9-point higher infant diet score (p = 0.001) at age 1-year, and lower maternal social disadvantage was associated with a 0.17-point higher infant diet quality score (p = 0.04). Conclusion: This study highlights the positive impact of health care provision by Indigenous midwives and confirms that higher maternal social advantage has a positive impact on child nutrition.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".