Epidemiology of Interpersonal Trauma among Women and Men Psychiatric Inpatients: A Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: Small clinical samples suggest that psychiatric inpatients report a lifetime history of interpersonal trauma. Since past experiences of trauma may complicate prognosis and treatment trajectories, population-level knowledge is needed about its prevalence and correlates among inpatients. METHODS: Using health-administrative databases comprising all adult psychiatric inpatients in Ontario, Canada (2009 to 2016, n = 160,436, 49% women), we identified those who reported experiencing physical, sexual, and/or emotional trauma in their lifetime, 1 year, and 30 days preceding admission. We described the prevalence of each type of trauma, comparing women and men using modified Poisson regression, and identified individual-level characteristics associated with lifetime trauma history using multivariable logistic regression. RESULTS: 31.7% of inpatients reported experiencing trauma prior to admission. Lifetime prevalence was higher in women (39.6% vs. 24.1%; age-adjusted prevalence ratio [aPR] = 1.68; 95% CI, 1.65 to 1.71), including sexual (22.7% vs. 8.4%; aPR = 2.81; 95% CI, 2.73 to 2.89), emotional (33.3% vs. 19.4%; aPR = 1.76; 95% CI, 1.72 to 1.79), and physical trauma (24.2% vs. 14.8%; aPR = 1.68; 95% CI, 1.65 to 1.72). Factors most prominently associated with lifetime trauma were witnessing parental substance use (adjusted odds ratio [aOR] = 8.68; 95% CI, 8.39 to 8.99), female sex (aOR = 2.29; 95% CI, 2.23 to 2.35), and number of recent stressful life events (aOR = 1.62; 95% CI, 1.59 to 1.65). CONCLUSIONS: These results suggest that trauma-informed approaches are essential to consider in the design and delivery of inpatient psychiatric services for both women and men.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".