Predictors of no, low and frequent emergency department use for any medical reason among patients with cannabis‐related disorders attending Quebec (Canada) addiction treatment centres
Bibliographic record
Abstract
INTRODUCTION: Patients with substance-related disorders and mental disorders (MD) contribute substantially to emergency department (ED) overcrowding. Few studies have identified predictors of ED use integrating service use correlates, particularly among patients with cannabis-related disorders (CRD). This study compared predictors of low (1-2 visits/year) or frequent (3+ visits/year) ED use with no ED use for a cohort of 9836 patients with CRD registered at Quebec (Canada) addiction treatment centres in 2012-2013. METHODS: This longitudinal study used multinomial logistic regression to evaluate clinical, sociodemographic and service use variables from various databases as predictors of the frequency of ED use for any medical reason in 2015-2016 among patients with CRD. RESULTS: Compared to non-ED users with CRD, frequent ED users included more women, rural residents, patients with serious MD and chronic CRD, dropouts from programs in addiction treatment centres and with less continuity of physician care. Compared with non-users, low ED users had more common MD and there more workers than students. DISCUSSION AND CONCLUSIONS: Multimorbidity, including MD, chronic physical illnesses and other substance-related disorders than CRD, predicted more ED use and explained frequent use of outpatient services and prior specialised acute care, as did being 12-29 years, after controlling for all other covariates. Better continuity of physician care and reinforcement of programs like assertive community or integrated treatment, and chronic primary care models may protect against frequent ED use. Strategies like screening, brief intervention and treatment referral, including motivational therapy for preventing treatment dropout may also be expanded to decrease ED use.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".