The long term mental health consequences of genocides on survivors' offspring
Bibliographic record
Abstract
Background The long term mental health consequences of genocides on survivors' offspring are increasingly discussed but conclusions have been conflicting. Methods We systematically reviewed studies from five electronic databases (EMBASE, PILOTS, PUBMED, PsycINFO, Web of Science) that used a quantitative study design and included: (i) exposure to the genocides of Armenians, in Nazi-Germany, Cambodia, Rwanda, and Bosnia); (ii) mental health outcomes; (iii) validated instruments; (iv) statistical tests of associations. Study quality was appraised using a quality assessment tool for genocide studies. PRISMA reporting guidelines were followed. Results From 3352 retrieved records, 20 studies with a total of 4793 participants involving 2431 children of survivors and 2362 controls met the eligibility criteria. Studies were conducted in seven countries: Australia, Canada, Italy, Israel, Norway, Rwanda, and the United States over the past seven decades. Data provide no consistent evidence that survivors offspring are more likely to have mental health problems than comparators who were not children of genocide survivors. Conclusions Future studies of the long term impact of genocides on mental health should report using a standardized structure.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 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".