Suicidality and mood disorders in psychiatric emergency patients: Results from SBQ‐R
Bibliographic record
Abstract
Patients with mood disorders are at high risk of suicidality, and emergency departments (ED) are essential in the management of this risk. This study aims to (1) describe the suicidal thoughts and behaviours of patients with mood disorders who come to ED; (2) assess the psychometric properties of the Suicidal Behaviours Questionnaire-Revised (SBQ-R) in a psychiatric ED; and (3) determine the best predictors of suicidality for these patients. A total of 300 participants with mood disorders recruited for the Signature Bank of the Institut universitaire en santé mentale de Montréal (IUSMM) were retained. Suicidality was assessed using the SBQ-R. Other clinical and demographic details were recorded. Bivariate analyses, correlations and multivariate regression analyses were conducted. SBQ-R's internal consistency, construct and convergent validities were also tested. In the Patient Health Questionnaire-9 (PHQ-9), 53.3% of the sample stated they had suicidal or self-harm thoughts in the last 2 weeks. The mean score obtained at the SBQ-R was 8.3. Multivariate analysis found that SBQ-R scores were associated with depressive symptoms and substance use, especially alcohol, accounting for 44.3% of the model variance. Cronbach's alpha was 0.81 [0.78, 0.84] and factor loadings for items 1-4 were 0.68, 0.88, 0.54, and 0.85, respectively. The confirmatory factor analysis indicated that the model fit the data well. The SBQ-R is a brief and valid instrument that can easily be used in busy emergency departments to assess suicide risk. Depressive symptoms and alcohol use shall also be assessed, as they are determinants of increased risk of suicidality.
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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 | 0.003 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".