Predictors of Recurrent High Emergency Department Use among Patients with Mental Disorders
Bibliographic record
Abstract
Few studies have examined predictors of recurrent high ED use. This study assessed predictors of recurrent high ED use over two and three consecutive years, compared with high one-year ED use. This five-year longitudinal study is based on a cohort of 3121 patients who visited one of six Quebec (Canada) ED at least three times in 2014–2015. Multinomial logistic regression was performed. Clinical, sociodemographic and service use variables were identified based on data extracted from health administrative databases for 2012–2013 to 2014–2015. Of the 3121 high ED users, 15% (n = 468) were recurrent high ED users for a two-year period and 12% (n = 364) over three years. Patients with three consecutive years of high ED use had more personality disorders, anxiety disorders, alcohol or drug related disorders, chronic physical illnesses, suicidal behaviors and violence or social issues. More resided in areas with high social deprivation, consulted frequently with psychiatrists, had more interventions in local community health service centers, more prior hospitalizations and lower continuity of medical care. Three consecutive years of high ED use may be a benchmark for identifying high users needing better ambulatory care. As most have multiple and complex health problems, higher continuity and adequacy of medical care should be prioritized.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".