Indigenous Women’s Stress and Postpartum Depression: Discussions from the Canadian Maternity Experiences Survey and Indigenous Maternity Narratives
Bibliographic record
Abstract
This doctoral research highlights Indigenous women’s experiences of stress and postpartum depression (PPD) through secondary quantitative analysis of the Public Health Agency of Canada’s Canadian Maternity Experiences Survey (2009) and qualitative Indigenous maternity narratives. Indigenous women’s responses to the survey demonstrate that Indigenous women experience 1.9 times higher odds of PPD and are 1.5 times as likely to be diagnosed with depression prior to pregnancy, compared to Canadian women. Indigenous women are also 1.3 times as likely to experience higher levels of stress and 3.3 times as likely to experience three or more stressful life events. While the survey demonstrates higher rates of stress and PPD, it is not culturally or contextually relevant. Therefore, Indigenous maternity narratives from 10 Indigenous mothers in 2015 further contextualize experiences of stress and PPD to include narratives related to adverse social determinants of health and impacts of colonialism. The research includes a discussion on the limitations of previous maternity research and the limits of clinical-medical assessments and diagnosis of stress and PPD in Indigenous populations. The research concludes with recommendations for additional maternity experiences research and ways to support Indigenous women, infants and children, birth partners, families, and communities.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".