Psychopathology in the Offspring of Indigenous Parents with Mental Health Challenges: A Systematic Review: Psychopathologie des descendants de parents autochtones ayant des problèmes de santé mentale: Une revue systématique
Bibliographic record
Abstract
OBJECTIVE: Parental psychopathology is a significant risk factor for mental health challenges in offspring, but the nature and magnitude of this link in Indigenous Peoples is not well understood. This systematic review examined the emotional and behavioral functioning of the offspring of Indigenous parents with mental health challenges. METHOD: We searched MEDLINE, EMBASE, PsycINFO, CINAHL, and Web of Science from their inceptions until April 2020. Studies were included if they included assessments of emotional, behavioral, or other psychological outcomes in the offspring of Indigenous parents with a mental health challenge. RESULTS: = 4). In 11 studies, parental substance misuse, depression, and/or overall mental health challenges were associated with 2 to 4 times the odds of offspring externalizing and internalizing behaviors as compared to offspring of Indigenous parents without mental health challenges. CONCLUSION: The findings suggest higher risks of mental health challenges among offspring of Indigenous parents with psychiatric difficulties than among Indigenous children of parents without similar difficulties. Knowledge of these phenomena would be improved by the use of larger, more representative samples, culturally appropriate measures, and the engagement of Indigenous communities. Future studies should be focused on both risk and resilience mechanisms so that cycles of transmission can be interrupted and resources aimed at detection, prevention, and treatment optimally allocated.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".