Emergency health care use and follow-up among sociodemographic groups of children who visit emergency departments for mental health crises
Bibliographic record
Abstract
BACKGROUND: Previous studies of differences in mental health care associated with children's sociodemographic status have focused on access to community care. We examined differences associated with visits to the emergency department. METHODS: We conducted a 6-year population-based cohort analysis using administrative databases of visits (n = 30,656) by children aged less than 18 years (n = 20,956) in Alberta. We measured differences in the number of visits by socioeconomic and First Nations status using directly standardized rates. We examined time to return to the emergency department using a Cox regression model, and we evaluated time to follow-up with a physician by physician type using a competing risks model. RESULTS: First Nations children aged 15-17 years had the highest rate of visits for girls (7047 per 100,000 children) and boys (5787 per 100,000 children); children in the same age group from families not receiving government subsidy had the lowest rates (girls: 2155 per 100,000 children; boys: 1323 per 100,000 children). First Nations children (hazard ratio [HR] 1.64; 95% confidence interval [CI] 1.30-2.05), and children from families receiving government subsidies (HR 1.60, 95% CI 1.30-1.98) had a higher risk of return to an emergency department for mental health care than other children. The longest median time to follow-up with a physician was among First Nations children (79 d; 95% CI 60-91 d); this status predicted longer time to a psychiatrist (HR 0.47, 95% CI 0.32-0.70). Age, sex, diagnosis and clinical acuity also explained post-crisis use of health care. INTERPRETATION: More visits to the emergency department for mental health crises were made by First Nations children and children from families receiving a subsidy. Sociodemographics predicted risk of return to the emergency department and follow-up care with a physician.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".