Mental Health Problems in Children and Caregivers in the Emergency Department Setting
Bibliographic record
Abstract
INTRODUCTION: Although mental health problems are increasing in the primary care sector, the prevalence of mental health problems in families presenting for nonpsychiatric complaints in the emergency department (ED) setting is generally unknown. As such, we set out to assess the frequency of mental health concerns and associated risk factors in children presenting for care in a pediatric ED. METHODS: A total of 411 mother-child dyads were randomly selected during a 2-year period from the less acute area of a large pediatric ED. Mothers were interviewed for child mental health concerns using structured diagnostic instruments. Mothers were also interviewed for their own mental health symptoms. Risk factor analysis for the outcome of a pediatric mental health concern was performed using bivariate and multivariate techniques. RESULTS: Of all children, 45% met criteria for a mental health concern, with 23% of all children meeting criteria for two or more mental health concerns; 21% of mothers screened positive for a mental health problem themselves. Once adjusted, children whose mothers' screened positive for a mental illness were more likely to have a mental health concern themselves. CONCLUSION: There is a large burden of mental health concerns in children and their mothers presenting to the ED for medical care. Efficiently and accurately identifying mental illness in children presenting to a pediatric ED is the first step in the intervention process for a population that might otherwise slip through the system.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".