Household food insecurity is prevalent in a cohort of postpartum women who registered in the Canada Prenatal Nutrition Program in Toronto
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence, severity and socio-demographic predictors of household food insecurity among vulnerable women accessing the Canada Prenatal Nutrition Program (CPNP) and to examine associations between household food insecurity and breastfeeding practices to 6 months. DESIGN: Cohort investigation pooling data from two studies which administered the 18-item Household Food Security Survey Module at 6 months postpartum and collected prospective infant feeding data at 2 weeks and 2, 4 and 6 months. Household food insecurity was classified as none, marginal, moderate or severe. Logistic regression analyses were performed to assess predictors of household food insecurity and associations between household food security (any and severity) and continued and exclusive breastfeeding. SETTING: Three Toronto sites of the CPNP, a federal initiative targeting socially and/or economically vulnerable women. PARTICIPANTS: 316 birth mothers registered prenatally in the CPNP from 2017 to 2020. RESULTS: < 0·001) and was higher among multiparous participants (OR 2·08; 95 % CI 1·28, 3·39). There was no association between the prevalence or severity of food insecurity and continued or exclusive breastfeeding to 6 months postpartum in the adjusted analyses. CONCLUSIONS: Household food insecurity affected nearly half of this cohort of women accessing the CPNP. Further research is needed on household food insecurity across the national CPNP and other similar programmes, with consideration of the implications for programme design, service delivery and policy responses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".