The Mental Health of Indigenous Perinatal Individuals, Associations of Parent and Offspring Psychopathology, and Offspring Risk and Well-being
Bibliographic record
Abstract
Objectives: To examine perinatal mental health, mechanisms of psychopathology transmission from parent to offspring, and offspring risk and well-being among Indigenous peoples by: 1) synthesizing the prevalence of perinatal mental health challenges among Indigenous perinatal individuals, 2) examining the association of psychopathology among Indigenous parent-offspring dyads, and 3) identifying factors associated with First Nations children’s well-being Methods: Four studies were designed to address these objectives. Study 1 meta-analyzed studies on mental health challenges among Indigenous pregnant and postpartum individuals. Study 2 used data from administrative health databases to identify the prevalence and determinants of depression, anxiety, and post-traumatic stress disorder among Métis pregnant persons in Alberta, Canada. Study 3 systematically synthesized studies on the association of Indigenous parent and offspring psychopathology. Study 4 used data from the 2006 Aboriginal Children’s Survey to identify determinants of First Nations children’s socioemotional and behavioural well-being Results: In Study 1, Indigenous perinatal individuals were at a 62% increased risk of a mental health challenge compared to non-Indigenous individuals. In Study 2, Métis pregnant persons were more likely to have depression, anxiety, and post-traumatic stress disorder than non-Métis pregnant persons. Factors associated with both depression and anxiety included having pre-pregnancy medical conditions, smoking/alcohol use/recreational substance use during pregnancy, and living in an urban location. In Study 3, offspring of Indigenous parents with mental health challenges were 2-4 times more likely to experience psychopathology compared to offspring of healthy Indigenous parents. In Study 4, knowledge of an Indigenous culture and strong community cohesion were associated with better well-being among First Nations children Conclusion: This work highlights the importance of reducing mental health challenges among Indigenous birthing parents and children and lends insight into cultural factors that can be used to promote the well-being of young First Nations children.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".