Association between mood disorders and frequent emergency department use: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: Frequent emergency department (ED) use is a growing problem that is associated with poor patient outcomes and increased health care costs. Our objective was to analyze the association between mood disorders and the incidence of frequent ED use. METHODS: We used the Canadian Community Health Survey conducted by Statistics Canada, 2015-2016. Mood disorder was defined as depression, bipolar disorder, mania, or dysthymia. Frequent ED use was defined as 4 or more visits in the year preceding the interview. Multivariable log-binomial regression models were used to determine the associations between mood disorders and frequent ED use. RESULTS: Among the 99,009 participants, 8.4% had mood disorders, 80.3% were younger than 65, and 2.2% were frequent ED users. Mood disorders were significantly associated with the 1-year cumulative incidence of frequent ED use (RR = 2.5, 95% CI 2.2-2.7), after adjusting for several potential confounders. CONCLUSIONS: This national survey showed that people with a mood disorder had a three-fold risk of frequent ED use, compared to people without mood disorder. These results can inform the development of policies and targeted interventions aimed at identifying and supporting ED patients with mood disorder.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".