Sex-differences in determinants of suicide risk preceding psychiatric admission: An electronic medical record study
Bibliographic record
Abstract
ABSTRACT Using electronic medical record (EMR) data collected from psychiatric inpatient admissions, the objective of this study was to identify sex differences in risk factors for presence of suicide plans and/or attempts within the 30 days preceding hospital admission. Resident Assessment Instrument for Mental Health (RAI-MH) intake data were obtained for patients admitted to a Canadian tertiary-care hospital deemed a ‘threat or danger to self’ during a ten-year period (2008-2018). Data was extracted for individuals categorized into three groups: non-suicidal (N=568), presence of suicide plan (N=178), and presence of suspected suicide attempt (N=124) in the 30 days prior to hospital admission. A multivariate logistic regression revealed that younger age (odds ratio=0.97), female sex (OR=1.56), disrupted family relationships (OR=1.54), recent stressors (OR=1.59), participation in social activities (OR=1.54), having no confidant (OR=1.55), and diagnosis of depressive disorder (OR=5.54) increased the odds of suicide plan and/or attempt in the 30 days prior to hospital admission. Stratifying the regression model by sex highlighted different risk factors for suicide plan and attempt specific to males and females. EMR-derived findings highlight psychosocial and clinical determinants associated with suicide plan or attempt prior to psychiatric admission that differ according to sex.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".