Intergenerational Trauma and Aboriginal Women: Implications for Mental Health During Pregnancy 1
Bibliographic record
Abstract
Intergenerational trauma explains why populations subjected to long-term and mass trauma show a higher prevalence of disease, even several generations after the original events. Residential schools and other legacies of colonization continue to impact Aboriginal populations, who have higher rates of mental health concerns. Poor maternal mental health during pregnancy can have serious health consequences for the mother, the baby, and the whole family; these include impacting the cognitive, emotional, and behavioural development of children and youth. This paper has the following objectives: to define intergenerational trauma and contextualize it in understanding the mental health of pregnant and parenting Aboriginal women; to summarize individual-level and population-level approaches to promoting mental health and examine their congruence with the needs of Aboriginal populations; and to discuss the importance of targeting intergenerational trauma in both individual-level and population-level interventions for pregnant Aboriginal women. Various scholars have suggested that healing from intergenerational trauma is best achieved through a combination of mainstream psychotherapies and culturally-entrenched healing practices, conducted in culturally safe settings. Pregnancy has been argued to be a particularly meaningful intervention point to break the cycle of intergenerational trauma transmission. Given the importance of pregnant women’s mental health to both maternal and child health outcomes, including mental health trajectories for children and youth, it is clear that interventions, programs, and services for pregnant Aboriginal women need to be designed to explicitly facilitate healing from intergenerational trauma. In this regard, further empirical research on intergenerational trauma and on healing are warranted, to permit an evidence-based approach.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".