High Frequency Users with Mental Health Complaints of Emergency Departments in a Canadian Prairie City
Bibliographic record
Abstract
Excessive use of emergency departments (EDs) for the treatment of mental health complaints (MHCs) suggests alternate interventions are required. To understand attributes of patients who are high frequent users (HFUs) of EDs for the treatment for MHCs. Suggest realistic changes that would make the health care system more effectively respond to HFUs needs. Administrative data were used to analyze the use of all three emergency departments in Saskatoon Health Region by patients presenting with primary MHCs in 2012. A chart review of HFUs for the years 2011 to 2013 was also undertaken. In 2012 approximately 1.2% (3,824) of the Region’s population made the 6,235 visits for a primary MHC. ED visits exhibit a Pareto distribution with most patients (72.8%) making a single visit however < 0.1 % (34) made 10+ visits. HFUs (10+ visits) used EDs in multiple years, over 3 years making an average of 59 ED visits (R=19 to 194). They were generally, young, male, unemployed, transient or homeless with many unmet social and economic needs. They could be categorized into: 1) suffering from severe alcohol abuse/withdrawal or illicit drug use; 2) chronic mood and anxiety disorders; 3) significant personal and social stressors; and 4) a complex combination of psychiatric disease(s) and cognitive impairment. Action should be taken to identify ‘High Frequency Users’ early in their patient career and develop appropriate interventions taking into account their unique and diverse needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".