The Perinatal Mental Health of Indigenous Women: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective: Although Indigenous women are exposed to high rates of risk factors for perinatal mental health problems, the magnitude of their risk is not known. This lack of data impedes the development of appropriate screening and treatment protocols, as well as the proper allocation of resources for Indigenous women. The objective of this systematic review and meta-analysis was to compare rates of perinatal mental health problems among Indigenous and non-Indigenous women. Methods: We searched Medline, EMBASE, PsycINFO, CINAHL, and Web of Science from their inceptions until February 2019. Studies were included if they assessed mental health in Indigenous women during pregnancy and/or up to 12 months postpartum. Results: Twenty-six articles met study inclusion criteria and 21 were eligible for meta-analysis. Indigenous identity was associated with higher odds of mental health problems (odds ratio [ OR] 1.62; 95% confidence interval [CI], 1.25 to 2.11). Odds were higher still when analyses were restricted to problems of greater severity ( OR 1.95; 95% CI, 1.21 to 3.16) and young Indigenous women ( OR 1.86; 95% CI, 1.51 to 2.28). Conclusion: Indigenous women are at increased risk of mental health problems during the perinatal period, particularly depression, anxiety, and substance misuse. However, resiliency among Indigenous women, cultural teachings, and methodological issues may be affecting estimates. Future research should utilize more representative samples, adapt and validate diagnostic and symptom measures for Indigenous groups, and engage Indigenous actors, leaders, and related allies to help improve the accuracy of estimates, as well as the well-being of Indigenous mothers, their families, and future generations. Trial Registration: PROSPERO-CRD42018108638.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".