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Record W2113989031 · doi:10.1176/ps.2006.57.4.477

Research on Day Treatment Programs for Preschoolers With Disruptive Behavior Disorders

2006· review· en· W2113989031 on OpenAlexaff
Jeanie Tse

Bibliographic record

VenuePsychiatric Services · 2006
Typereview
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsMcGill University
Fundersnot available
KeywordsDay treatmentPsychologyMedicineDevelopmental psychologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: This review examined the literature on psychosocial treatments for preschoolers with disruptive behavior disorders to delineate ways in which interventions developed in research settings can be used to enhance existing clinical preschool day treatment programs. METHODS: A PsycINFO search was performed by using the keywords "day treatment," "behavior problems," "disruptive behavior disorder," and "oppositional defiant disorder" to find articles published between 1974 and 2004 on randomized controlled trials of psychosocial interventions for preschoolers (ages two to five years). No randomized controlled trials of multimodal day treatment programs were found. Therefore, studies on multimodal day treatment were selected on the basis of whether they presented quantitative outcome data. RESULTS AND CONCLUSIONS: Little research was available on the effectiveness of day treatment programs, despite the programs' prevalence as a treatment modality for disruptive preschoolers. In contrast, many interventions in the disruptive behavior disorder research have not made their way into clinical practice. Preschool day treatment programs may be enhanced by improving access to care, emphasizing social problem-solving skills, and using strategies to engage families in treatment. Research studies on existing day treatment programs are needed to clarify the role of these programs in child psychiatry clinics and to shed light on optimal methods of service delivery.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.083
GPT teacher head0.422
Teacher spread0.338 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2006
Admission routes1
Has abstractyes

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