Prevalence rate, demographic and clinical predictors of substance use disorders in emergency room psychiatric patients of a tertiary hospital in Canada
Bibliographic record
Abstract
Background There is only a limited body of literature which has examined the factors which can predict the presence of substance use disorders (SUD) in psychiatric patients seeking emergency room (ER) treatment. Objective To examine the factors that can predict the likelihood that a patient presenting to the emergency room and referred to the liaison psychiatric team will suffer from a SUD. Methods Nineteen independent demographic and clinical factors from data assessment tools for 477 patients assessed by the liaison psychiatric team in the ER over 12 months were compiled and analysed using univariate analyses and logistic regression in SPSS (version 20). Results The 12-month prevalence rate of all SUDs in our clinical sample was 24.7%. Patients who presented to the ER with a chief alcohol and/or drug related complaint (withdrawal or intoxication) were 142 times more likely to fulfill the diagnostic criteria for SUD compared to those who presented with non-SUD related complaints. Male patients or patients with forensic history were both three times more likely to suffer from SUD than female patients or patients with no forensic history, respectively. Conclusion There is a high prevalence of SUDs among psychiatric patients assessed in the ER. In addition to targeting patients who present to the ER with an alcohol or drug withdrawal/intoxication for brief psycho-educational interventions and referrals to addiction treatment services, patients with forensic history and male patients should be targeted for SUD screening. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".