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Record W3216618175 · doi:10.1007/s10488-021-01179-7

Patterns of Child Mental Health Service Utilization Within a Multiple EBP System of Care

2021· article· en· W3216618175 on OpenAlexaff
Joyce H. L. Lui, Lauren Brookman‐Frazee, Alejandro Vázquez, Julia R. Cox, Debbie Innes-Gomberg, Kara Taguchi, Keri Pesanti, Anna S. Lau

Bibliographic record

VenueAdministration and Policy in Mental Health and Mental Health Services Research · 2021
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsChild, Adolescent and Family Mental Health
FundersNational Institute of Mental Health
KeywordsMental healthService (business)Mental health serviceMental health carePsychologyNursingMedicinePsychiatryBusiness

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.449
Teacher spread0.390 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2021
Admission routes1
Has abstractyes

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