Emergency Department as a First Contact for Mental Health Problems in Children and Youth
Bibliographic record
Abstract
OBJECTIVE: To characterize youth who use the emergency department (ED) as a "first contact" for mental health (MH) problems. METHOD: This was a population-based cross-sectional cohort study using linked health and demographic administrative datasets of youth 10 to 24 years of age with an incident MH ED visit from April 1, 2010, to March 31, 2014, in Ontario, Canada. We modeled the association of demographic, clinical, and health service use characteristics with having no prior outpatient MH care in the preceding 2-year period ("first contact") using modified Poisson models. RESULTS: Among 118,851 youth with an incident mental health ED visit, 14.0% were admitted. More than half (53.5%) had no prior outpatient MH care, and this was associated with younger age (14-17 versus 22-24 years old: risk ratio [RR] = 1.09, 95% CI = 1.07-1.10), rural residence (RR = 1.16, 95% CI = 1.14-1.18), lowest versus highest income quintile (RR = 1.04, 95% CI = 1.03-1.06), and refugee immigrants (RR = 1.17, 95% CI = 1.13-1.21) and other immigrants (RR = 1.10, 95% CI = 1.08-1.13) versus nonimmigrants. The 5.1% of the cohort without a usual provider of primary care had the highest risk of first contact (RR = 1.78, 95% CI = 1.77-1.80). A history of low-acuity ED use and individuals whose primary care physicians were in the lowest tertile for mental health visit volumes were associated with higher risk. CONCLUSION: More than half of youth requiring ED care had not previously sought outpatient MH care. Associations with multiple markers of primary care access characteristics suggest that timely primary care could prevent some of these visits.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".