Exploring the relationships between food insecurity, maternal stress, and maternal–infant health outcomes during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract The COVID-19 pandemic intensified food insecurity (FI) and stress for many pregnant individuals, which may have contributed to adverse fetal developmental programming. This study aimed to identify key social determinants of health associated with pandemic-related FI and stress, and their association with gestational weight gain (GWG) and newborn birth weight in a Canadian pregnant cohort. Data were collected retrospectively from 273 pregnant individuals who delivered infants in Canada during the pandemic (March 2020–March 2023). Validated questionnaires were used to assess FI and pandemic-related stress, and GWG and infant birth weight were self-reported. FI was experienced by 55.7% of the participants, while 33.7% and 19.7% reported heightened stress related to COVID-19 infection and pregnancy preparedness, respectively. Participants from food-secure and food-insecure households differed significantly in parental structure, age, sexual orientation, housing status, household income, number of children in the household and pregnancy planning (all p values < 0.01). Heightened stress for both pregnancy preparedness and COVID-19 infection was also significantly associated with these same factors (all p values < 0.05) but not for age and housing status. FI and heightened stress were not associated with GWG outside the recommended range. However, significantly higher likelihood of birth weight extremes was observed with heightened COVID-19 infection-related stress (OR, 95% CI 1.50, 1.05–2.12, p = 0.02) and pregnancy preparedness-related stress (1.60, 1.10–2.31, p = 0.01), but not with FI. These findings underscore the influence of psychosocial factors on FI and stress during pregnancy, which may negatively impact infant health outcomes during the pandemic.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".