Profiles of Emergency Department Users with Psychiatric Disorders Related to Barriers to Outpatient Care
Bibliographic record
Abstract
Emergency department (ED) overcrowding is a growing problem worldwide. High ED users have been historically targeted to reduce ED overcrowding and associated high costs. Patients with psychiatric disorders, including substance-related disorders (SRDs), are among the largest contributors to high ED use. Since EDs are meant for urgent cases, they are not an appropriate setting for treating recurrent patients or replacing outpatient care. Identifying ED user profiles in terms of perceived barriers to care, service use, and sociodemographic and clinical characteristics is crucial to reduce ED use and unmet needs. Data were extracted from medical records and a survey was conducted among 299 ED patients from 2021 to 2022 in large Quebec networks. Cluster algorithms and comparison tests identified three profiles. Profile 1 had the most patients without barriers to care, with case managers, and received the best primary care. Profile 2 reported moderate barriers to care and low primary care use, best quality of life, and more serious psychiatric disorders. Profile 3 had the most barriers to care, high ED users, and lower service satisfaction and perceived mental/health conditions. Our findings and recommendations inform decision-makers on evidence-based strategies to address the unmet needs of these vulnerable populations.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".