Prevalence of psychiatric diagnoses among frequent users of rural emergency medical services.
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine whether there was an increased prevalence of psychiatric disorders among frequent users of rural emergency medical services. METHODS: In a matched comparison design, I compared frequent users of the emergency departments of 2 rural hospitals, both affiliated with an academic centre, with randomly selected users and with randomly selected users who had the same medical diagnoses. The main outcome measures were psychiatric diagnoses on a structured clinical interview, along with medical diagnoses and number of emergency department visits in the past year. RESULTS: Ninety-three percent of frequent users had at least 1 DSM-IV psychiatric diagnosis, differing from 50% of random users matched for presenting complaint. A random user group, not matched for presenting complaint, showed 28% prevalence of DSM-IV diagnoses. Frequent users were more often state insured (Medicaid) and less often insured privately. The most common diagnoses among frequent users were major depression, generalized anxiety disorder, adjustment disorder, somatoform pain disorder, substance abuse and dependence, and dysthymia. The treating emergency department physician mentioned a psychiatric diagnosis for only 9% of frequent users. CONCLUSION: Frequent users have a disproportionately high prevalence of psychiatric disorders (under-documented by their physicians), which may affect their pattern of emergency department use. This suggests the need for better recognition, diversion, and management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".