Predictors of Postpartum Depression Among Immigrant Women in the Year After Childbirth
Bibliographic record
Abstract
BACKGROUND: Immigrant women are at increased risk for postpartum depression (PPD). The factors that influence PPD among immigrant women are poorly understood. The purpose of this study was to identify individual- and community-level factors predictive of PPD among immigrant women living in a large Ontario city at 6 weeks, 6 months, and 1 year postpartum. METHODS: The study involved a secondary analysis of a prospective cohort study, The Ontario Mother and Infant Study 3. This study included 519 immigrant women who were recruited from two hospitals in one urban city and delivered full-term singleton infants. Women completed a written questionnaire in hospital, followed by structured telephone interviews at 6 weeks, 6 months, and 1 year after hospital discharge. Generalized estimating equations were used to explore factors associated with PPD, measured using the Edinburgh Postnatal Depression Scale (EPDS) and two thresholds for depression (≥12 and ≥9). RESULTS: Rates of PPD at all time points were 8%-10% for EPDS scores of ≥12. For EPDS scores of ≥9, rates of PPD more than doubled at all time points. A lack of social support was strongly associated with PPD in all analyses. Living in Canada for ≤2 years, poor perceptions of health, and lower mental health functioning were other important predictors of PPD. Living in communities with a high prevalence of immigrants and low income also was associated with PPD. CONCLUSIONS: Complex individual and community-level factors are associated with PPD in immigrant women. Understanding these contextual factors can inform a multifaceted approach to addressing PPD.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".