Frequent use of emergency departments for mental and substance use disorders
Bibliographic record
Abstract
OBJECTIVES: We described the population of people who frequently use ED for mental disorders, delineating differences by the number of visits for substance use disorders (SUDs), and predicted the receipt of follow-up services and 2-year mortality by the level of ED use for SUD. METHODS: This retrospective observational study included all Ontario residents 15 years and older who had five or more ED visits during any 12-month period from 2010 to 2012 (n=263 346). The study involved a secondary analysis of administrative health databases capturing emergency, hospital and ambulatory care. Frequent ED users for mental disorders (n=5416) were grouped into nested categories based on the number of ED visits for SUD. Logistic regression was used to examine group differences in the receipt of follow-up services and mortality, controlling for sociodemographics, comorbidities and past service use. RESULTS: The majority of frequent ED users for mental disorders had at least one ED visit for SUD, most commonly involving alcohol. Relative to people with no visits for SUD, those with ED visits for SUD were older and more likely to be men (Ps <0.001). As the number of ED visits for SUD increased, the likelihood of receiving follow-up care, particularly specialist mental healthcare, declined while 2-year mortality steadily increased (Ps <0.001). These associations remained after controlling for comorbidities and past service use. CONCLUSIONS: Findings highlight disparities in the receipt of specialist care based on use of ED services for SUD, coupled with a greater mortality risk. There is a need for policies and procedures to help address unmet needs for care and to connect members of this vulnerable subgroup with services that are better able to support recovery and improve survival.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".