Risk for Maternal Depressive Symptoms and Perceived Stress by Ethnicities in Canada: From Pregnancy Through the Preschool Years
Bibliographic record
Abstract
Objective:Past cross-sectional studies have reported that mothers from ethnic minorities experience higher levels of prenatal and post-partum psychosocial distress compared with mothers from ethnic majorities. However, no studies have examined how the pattern varies longitudinally in a Canadian population of heterogeneous ethnicity.Methods:We analyzed data from 3,138 mothers participating in the Canadian Healthy Infant Longitudinal Development (CHILD) Study, a longitudinal multi-center study incorporating 10 distinct waves of psychosocial data collection from pregnancy until the index child was aged 5 y. Maternal self-identified ethnicity was grouped as White Caucasian, First Nations, Black, Southeast Asian, East Asian, South Asian, Middle Eastern, Hispanic and mixed ethnicity. We performed a multi-level regression to determine whether mothers of specific minority ethnicities were more likely to experience higher levels of distress (i.e. depressive symptoms and perceived stress) compared to white Caucasian mothers.Results:Mothers self-identifying as Black or First Nations had consistently higher distress scores than mothers from other ethnicities across all data collection times. After adjusting for relevant variables (history of depression, education, household income, marital status, and social support), First Nations mothers had a 20% increase in the mean scores of depressive symptoms compared to White Caucasian Mothers.Conclusions:Increased levels of perinatal and post-partum distress were seen in only some ethnic minority groups. Studies should avoid collapsing all categories into ethnic minority or majority and may need to consider how ethnicity interacts with other sociodemographic factors such as poverty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".