Pediatric Behavioral Health Screening in Primary Care
Bibliographic record
Abstract
BACKGROUND: Roughly 21% of youth in the United States meet criteria for a mental health diagnosis, but only one-fifth of those children obtain help. The Pediatric Behavioral Health Screen (PBHS) utilizes the Pediatric Symptom Checklist-17 (PSC-17) and functional impairment items to assess behavioral health concerns. METHODS: Data were obtained from a systematic chart review for children 6 to 16 years old. Descriptive analyses and a confirmatory factor analysis were used to evaluate the clinical performance and utility of the PBHS. RESULTS: A positive screen was endorsed for 26.7% of the sample, of whom 68% also experienced functional impairment. Clinicians appropriately administered the screen 73.5% of the time. The 3-factor model of the PSC-17 exhibited a good model fit. CONCLUSIONS: Prevalence rates of psychosocial concerns and functional impairment affirm the need for routine behavioral health screening in the pediatric primary care setting. The PBHS exhibited good psychometric performance and clinical utility.
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".