Population Attributable Fractions of Psychiatric Disorders and Suicide Ideation and Attempts Associated With Adverse Childhood Experiences
Bibliographic record
Abstract
OBJECTIVES: We sought to determine the fractions of psychiatric disorders and suicide ideation and attempts in a general population sample attributable to childhood physical abuse, sexual abuse, and witnessing domestic violence. METHODS: Data were obtained from the US National Comorbidity Survey Replication. Population attributable fractions were calculated to determine the proportion of psychiatric disorders and suicide ideation and attempts attributable to adverse childhood experiences. The analysis was stratified by gender. RESULTS: The estimated attributable fractions for psychiatric disorders attributable to having experienced any adverse childhood event ranged from 22% to 32% among women and 20% to 24% among men. Having experienced any adverse event accounted for a substantial proportion of suicide ideation and attempts among women (16% and 50%, respectively) and men (21% and 33%, respectively). Substantial proportions of poor mental health outcomes were also attributable to increasing number of adverse events. CONCLUSIONS: The estimated proportions of poor mental health outcomes attributed to childhood adversity were medium to large for men and women. Prevention efforts that reduce exposure to adverse childhood events could substantially reduce the prevalence of psychopathology and suicidal behavior in the general population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".