Frequent Mental Health and Addiction Related Emergency Department Visits: Perspectives from Healthcare Providers
Bibliographic record
Abstract
Background: The rise in mental health and addiction (MHA)-related emergency department (ED) visits has been recognized as a contributing factor to ED crises and increasing healthcare costs. While prior research has largely centered on patients' perspectives, limited attention has been given to healthcare providers’ (HCPs) insights. This qualitative study specifically explores HCPs’ perceptions of the reasons patients with MHA issues frequently present to EDs. Methods: HCPs were recruited from ED, and MHA services of the local health authority and community agencies. Data collection involved semi-structured individual interviews. The thematic analysis approach was utilized in data analysis. Results: Six HCPs from diverse disciplines participated in this qualitative study. Four major themes emerged from the data analysis: (a) social determinants of mental health (housing crisis and financial problems); (b) structural barriers (overstimulation and not a priority in ED, inadequate knowledge and training among HCPs, lack of detox facilities, stigma from HCPs, and shortages of HCPs); (c) suggestions for prevention (more funding/ resources and early childhood education) and (e) HCP’s response to working with patients (making a difference and rewarding). Implications and lessons learned: The findings indicate the importance of MHA specialty training for HCPs, combined with innovative anti-stigma initiatives. Nurses can play a crucial role in policy development focusing on enhancing MHA services, and ultimately reducing MHA-related emergency visits.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".