Care Trajectories Among Patients With Substance‐Related Disorders in the 3 Years Before Their Last Suicide Attempt
Bibliographic record
Abstract
OBJECTIVES: Among patients with substance-related disorders (SRDs) and suicide attempts in 2014-2021, we identified care trajectories within a 3-year period preceding their last suicide attempt (index date). We also determined associations between care trajectories and patients sociodemographic and clinical characteristics, quality of care received, and risk of death in the following year. METHODS: Using Quebec (Canada) medical databases, we produced Group-based Multi-Trajectory Modeling, Multinomial Logistic Regression, and Cox Proportional Hazards models. RESULTS: We identified five care trajectories (profiles) among the 2297 study patients. Profile 1 (31%, reference profile) had low outpatient care with late increase in acute care use. It included more men, younger patients and individuals with overall better conditions. Profile 2 (22%) received high physical healthcare and had more chronic physical illnesses. Profile 3 (19%), with more serious mental disorders, and Profile 4 (16%), with more polysubstance-related disorders, used more outpatient mental health (MH) or SRD care, decreasing before index date. Profiles 2, 3 and 4 increasingly used acute care before index date. Profile 5 patients (12%) had more health problems, and so showed the highest overall care use and highest risk of death. CONCLUSION: Tailored interventions are suggested for all profiles, with improved screening and SRD-MH care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".