Maternal Depression Mediated the Association Between Moderate Income Inequality and Physical Aggression in Five Year Old Children.
Bibliographic record
Abstract
Background: Pathways linking income inequality and health have been hypothesized, with few studies identifying the role of potential mediators pertaining to children's health. Objective: This study examined whether the association between income inequality and children's physical aggression was mediated by maternal depression, anxiety, or social support. Methods: Data were drawn from the All Our Families (AOF) longitudinal cohort based in Calgary, Canada, was used (n = 1090 mother-child dyads). Neighbourhood income inequality was measured via the Gini coefficient derived from the 2006 Canadian Census. Mothers completed the Center for Epidemiologic Scales for Depression, Spielberger State Trait Anxiety Inventory, and the Medical Outcomes Study Social Support Scales, respectively at 3 months postpartum. Maternal and child demographic characteristics were measured at 3 years postpartum. Children's physical aggression was assessed using the Behavior Assessment System for Children, Second Edition at 5 years postpartum. Multilevel path models were used. Results: Mothers in neighbourhoods with moderate income inequality had higher depression symptoms (b = 0.49, 95% CI: 0.01, 0.97) compared to mothers in the most equal neighbourhoods (low-income inequality). Maternal depression was subsequently associated with physical aggression in children (b = 0.26, 95% CI: 0.04, 0.47). Maternal social support was lower (b = -2.51, 95% CI: -4.53, -0.49) among mothers in neighbourhoods with high income inequality compared to mothers in the most equal neighbourhoods. Conclusion: The association between income inequality, maternal mental health, social support, and child physical aggression is complex. More research comprising larger, diverse samples of mother-child dyads may be required to clarify these associations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".