Child and Adolescent Mental Health Repeat Visits to the Emergency Department: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Repeat visits represent up to 45% of mental health (MH) presentations to emergency departments (EDs) and are associated with higher health care costs. We aimed to synthesize available literature on predictors of pediatric MH repeat ED visits and differences between repeat visitors and nonrepeat visitors. METHODS: A systematic review was performed using PsycINFO, PubMed, and CINAHL databases. Reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement checklist. Methodologic quality was assessed using the following 8 criteria: design, generalizability, breadth of predictors, reporting of effect sizes, additional outcomes, interaction terms, confounding variables, and clear definition of repeat visits. RESULTS: A total of 178 articles were retrieved; 11 articles met inclusion criteria. Quality assessment revealed that all studies used chart review or administrative data. Predictors were grouped into 3 categories: demographic, clinical, and MH care access and utilization factors. Common predictors associated with repeat ED MH visits included socioeconomic status, involvement with child protective services, and previous and current MH service use. For studies using a 6-month repeat window, the most common factors were previous psychiatric hospitalization and currently receiving MH services. Heterogeneity in statistical analyses and determinants explored precluded the use of meta-analysis. CONCLUSIONS: Findings revealed that repeat visits to the ED for MH concerns is a complex phenomenon that can be attributed to various demographic, clinical, and MH care access and utilization factors. To further elucidate the strongest predictors, future prospective research should consider prospective designs and include family factors. Investigating recency and frequency outcomes can also inform clinical practice.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".