Patterns of Child Mental Health Service Utilization Within a Multiple EBP System of Care
Bibliographic record
Abstract
The current study (1) characterizes patterns of mental health service utilization over 8 years among youth who received psychotherapy in the context of a community implementation of multiple evidence-based practices (EBPs), and (2) examined youth-, provider- and service-level predictors of service use patterns. Latent profile analyses were performed on 5,663,930 administrative claims data furnished by the county department of mental health. Multinomial logistic regression with Vermunt's method was used to examine predictors of care patterns. Based on frequency, course, cost, and type of services, three distinct patterns of care were identified: (1) Standard EBP Care (86.3%), (2) Less EBP Care (8.5%), and (3) Repeated/Chronic Care (5.2%). Youth age, ethnicity, primary language, primary diagnosis and secondary diagnosis, provider language and provider type, and caregiver involvement and service setting were significant predictors of utilization patterns. Although the majority of youth received care aligned with common child EBP protocols, a significant portion of youth (13.7%) received no evidence-based care or repeated, costly episodes of care. Findings highlight opportunities to improve and optimize services, particularly for youth who are adolescents or transition-aged, Asian-American/Pacific Islander, Spanish-speaking, or presenting with comorbidities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".