Characteristics for Low, High and Very High Emergency Department Use for Mental Health Diagnoses from Health Records and Structured Interviews.
Bibliographic record
Abstract
Introduction: Patients with mental health diagnoses (MHD) are among the most frequent emergency department (ED) users, suggesting the importance of identifying additional factors associated with their ED use frequency. In this study we assessed various patient sociodemographic and clinical characteristics, and service use associated with low ED users (1-3 visits/year), compared to high (4-7) and very high (8+) ED users with MHD. Methods: Our study was conducted in four large Quebec (Canada) ED networks. A total of 299 patients with MHD were randomly recruited from these ED in 2021-2022. Structured interviews complemented data from network health records, providing extensive data on participant profiles and their quality of care. We used multivariable multinomial logistic regression to compare low ED use to high and very high ED use. Results: Over a 12-month period, 39% of patients were low ED users, 37% high, and 24% very high ED users. Compared with low ED users, those at greater probability for high or very high ED use exhibited more violent/disturbed behaviors or social problems, chronic physical illnesses, and barriers to unmet needs. Patients previously hospitalized 1-2 times had lower risk of high or very high ED use than those not previously hospitalized. Compared with low ED users, high and very high ED users showed higher prevalence of personality disorders and suicidal behaviors, respectively. Women had greater probability of high ED use than men. Patients living in rental housing had greater probability of being very high ED users than those living in private housing. Using at least 5+ primary care services and being recurrent ED users two years prior to the last year of ED use had increased probability of very high ED use. Conclusion: Frequency of ED use was associated with complex issues and higher perceived barriers to unmet needs among patients. Very high ED users had more severe recurrent conditions, such as isolation and suicidal behaviors, despite using more primary care services. Results suggested substantial reduction of barriers to care and improvement on both access and continuity of care for these vulnerable patients, integrating crisis resolution and supported housing services. Limited hospitalizations may sometimes be indicated, protecting against ED use.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".