A syndemic perspective on food insecurity, gestational diabetes, and mental health disorders during pregnancy
Bibliographic record
Abstract
Pregnancy brings numerous physiological and psychosocial changes and conditions that may include gestational diabetes mellitus (GDM) and anxiety and mood disorders. Household food insecurity (HFI)-not having access to food that meets dietary needs and preferences-may put pregnant people at risk for developing pregnancy complications like GDM. This study used qualitative and quantitative methods to understand, from a syndemic perspective, the intersections among these conditions in Canada. Using the Canadian Community Health Survey cycles from 2009 to 2018, we fit multivariable and multivariate logistic regressions to these data to understand interactions among food insecurity, anxiety and mood disorders, and GDM. We also conducted four focus group discussions (FGDs) and six one-on-one interviews with pregnant and postpartum people living in Hamilton, Ontario. Analyses of the survey data show that pregnant individuals who reported an anxiety and/or mood disorder were more likely to experience HFI. Those who experienced HFI were also more likely to be diagnosed with GDM during pregnancy or report an anxiety and/or mood disorder. Major themes identified from interviews and FGDs revealed that structural variables impact access to food, that a GDM diagnosis increased anxiety, and that experiencing HFI exacerbates the management of these conditions during pregnancy. The potential interactions among HFI, GDM, and anxiety and/or mood disorders indicate that addressing rising HFI alongside prevention and treatment of GDM and anxiety and mood disorders are critical to improving the health and well-being of pregnant people 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".