Reasons Explaining High Emergency Department Use in Patients With Mental Illnesses: Different Staff Perspectives
Bibliographic record
Abstract
For patients with mental illnesses (MIs), emergency departments (EDs) are often the entry point into the healthcare system, or their only resort for quickly accessing mental health treatment. A better understanding of the various barriers justifying high ED use among patients with MIs may help recommend targeted interventions that better meet their needs. This explorative qualitative study aimed to identify such barriers and the solutions brought forth to reduce ED use based on the perspectives of clinicians and managers working in EDs, other hospital departments or the community sector. Interviews were conducted between April 2021 and February 2022; 86 mental health professionals (22% were nurses) from four large urban ED sites in Quebec (Canada) were interviewed. Barriers were identified in relation to patient profiles, healthcare system and organisational features and professional characteristics. The key barriers that were found to explain high ED use were patients having serious MIs (e.g., psychotic disorders) or social issues (e.g., poverty), lack of coordination and patient referrals between EDs and other health services, insufficient access to mental health and addiction services and inadequacy of care. Very few solutions were implemented to improve care for high ED users. Better deployment of ED interventions in collaboration with outpatient care may be prioritised to reduce high ED use for patients with MIs. Improvements to the referral and transfer processes to outpatient care, particularly through care plans and case management programs, may be implemented to reduce high ED use and improve outpatient care among patients with multiple health and social needs.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".