Social fragmentation and maternal mental health: a longitudinal analysis of depression and anxiety among pregnant women and mothers in Alberta, Canada
Bibliographic record
Abstract
Background: Maternal mental health is a significant public health concern, particularly during the perinatal period when depression and anxiety are prevalent. In Canada, approximately 23% of postpartum mothers report symptoms of depression or anxiety. Social fragmentation, characterized by weakened social ties and community cohesion, has been linked to mental health challenges. However, its impact on maternal well-being remains underexplored, particularly within the Canadian context. Objective: This study examines the association between neighborhood social fragmentation and maternal depression and anxiety, among mothers in Calgary, Alberta. The research aims to investigate whether higher levels of social fragmentation correspond with increased risks of depressive and anxiety symptoms. Methods: Using data from the All Our Families (AOF) cohort, this study analyzed 1,693 mothers from 158 neighborhoods, collected across four time points from early pregnancy (<25 weeks’ gestation) to one year postpartum. Social fragmentation was measured using the Congdon Index, derived from the 2006 Canadian Census. Multilevel growth curve modeling was employed to evaluate the relationship between social fragmentation and maternal depression and anxiety, while accounting for individual- and area-level covariates. Sensitivity analyses were performed to examine the robustness of the results. Results: Contrary to initial hypotheses, those who lived in neighbourhood with the highest level of neighborhood social fragmentation were significantly associated with lower depressive symptoms among mothers in the fully adjusted model (regression coefficient β = -0.76, 95% CI: -1.43, -0.08). This inverse relationship was not observed for anxiety symptoms (regression coefficient β = -1.01, 95% CI: -2.45, 0.44), suggesting that the social and contextual factors influencing depression and anxiety may differ. When depressive and anxiety symptoms were analyzed as dichotomous outcomes, no significant associations were found. Conclusion: These findings challenge conventional assumptions that social fragmentation uniformly predicts negative mental health outcomes. Instead, they underscore the nuanced relationship between social environment and maternal mental health, suggesting that fragmented neighborhoods may possess protective factors that warrant further exploration. Future research should aim to contextualize social fragmentation measures and investigate the community dynamics that might contribute to maternal resilience in fragmented settings.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".