Overlooked and Underserved: “Action Signs” for Identifying Children With Unmet Mental Health Needs
Bibliographic record
Abstract
OBJECTIVE: The US Surgeon General has called for new approaches to close the mental health services gap for the large proportion of US children with significant mental health needs who have not received evaluation or services within the previous 6 to 12 months. In response, investigators sought to develop brief, easily understood, scientifically derived "warning signs" to help parents, teachers, and the lay public to more easily recognize children with unmet mental health needs and bring these children to health care providers' attention for evaluation and possible services. METHOD: Analyses of epidemiologic data sets from >6000 children and parents were conducted to (1) determine the frequency of common but severely impairing symptom profiles, (2) examine symptom profile frequencies according to age and gender, (3) evaluate positive predictive values of symptom profiles relative to Diagnostic and Statistical Manual of Mental Disorders diagnoses, and (4) examine whether children with 1 or more symptom profiles receive mental health services. RESULTS: Symptom-profile frequencies ranged from 0.5% to 2.0%, and 8% of the children had 1 or more symptom profile. Profiles generated moderate-to-high positive predictive values (52.7%-75.4%) for impairing psychiatric diagnoses, but fewer than 25% of children with 1 or more profiles had received services in the previous 6 months. CONCLUSIONS: Scientifically robust symptom profiles that reflect severe but largely untreated mental health problems were identified. Used as "action signs," these profiles might help increase public awareness about children's mental health needs, facilitate communication and referral for specific children in need of evaluation, and narrow the child mental health services gap.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".