Breastfeeding and maternal physical and mental health among food insecure families with infants in Canada
Bibliographic record
Abstract
One in six children in Canada live in food insecure households, but we know very little about the early‐life conditions facing infants in these households. Data from the US finds infants living in food insecure households face higher risk of poor health, hospitalization, and psychosocial and developmental problems. Also in the US, food insecurity has been shown to increase risk of maternal depression and reduce odds of following recommended infant feeding practices. Using nationally representative data available from the 2007–2012 Canadian Community Health Survey, we examined the relationship between food insecurity, breastfeeding practices, and maternal self‐reported general and mental health among mothers of infants 0–24 months of age (n = 6,470). When controlling for sociodemographics and maternal mental health, mothers living in food insecure households, compared to those living in food secure households, were as likely to initiate breastfeeding (aOR: 1.11, 95% CI 0.70–1.76), but once initiated, they were more likely to stop exclusive breastfeeding earlier than recommended (<4 months (aOR: 1.59, 95% CI 1.17–2.17), <6 months (aOR: 1.51, 95% CI 1.05–2.17)). In multivariate analyses controlling for maternal‐and household‐level sociodemographics, household food insecurity independently predicted mothers of infants reporting self‐perceived poor general health (aOR: 3.43, 95% CI 1.88–6.26), poor mental health (aOR: 4.48, 95% CI 2.81–7.14), and high life stress (aOR: 2.21, 95% CI 1.73–2.82). This study suggests that food insecurity interferes with a mother's ability to optimally care for her infant, although these relationships are cross‐sectional. More research is needed to understand the impact of food insecurity on infant health in Canada.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".