Profiles of patients using emergency departments or hospitalized for suicidal behaviors
Bibliographic record
Abstract
OBJECTIVES: This study identified profiles of patients with suicidal behaviors, their sociodemographic and clinical correlates, and assessed the risk of death within a 12-month follow-up period. METHODS: Based on administrative databases, this 5-year study analyzed data on 5064 patients in Quebec who used emergency departments (ED) or were hospitalized for suicidal behaviors over a 2-year period. Latent class analysis was used for patient profiles, bivariate analysis for patient correlates over 2 years, and survival analysis for risk of death within a 12-month follow-up. RESULTS: Four profiles were identified: high suicidal behaviors and high service use (Profile 1: 23%); low suicidal behaviors and moderate service use (Profile 2: 46%); low suicidal behaviors and low service use (Profile 3: 25%); and high suicidal behaviors and high acute care, but low outpatient care (Profile 4: 6%). Profiles 1 and 4 patients had more serious conditions, with a higher risk of death in Profile 1 versus Profiles 2 and 3. Profile 2 patients had relatively more common mental disorders, and Profile 3 patients had less severe conditions. Profiles 3 and 4 included more men and younger patients. CONCLUSION: Programs better adapted to patient profiles should be deployed after ED use and hospitalization in coordination with outpatient services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".