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Record W2464636402 · doi:10.1002/jclp.22341

Treatment Outcome of Adolescent Inpatients With Early‐Onset and Adolescent‐Onset Disruptive Behavior

2016· article· en· W2464636402 on OpenAlexaff
Sjoukje Berdina Beike de Boer, Albert E. Boon, Fop Verheij, Marianne C. H. Donker, Robert Vermeiren

Bibliographic record

VenueJournal of Clinical Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsPsychologyPsychological interventionDrop outClinical psychologyChild Behavior ChecklistChecklistPediatricsPsychiatryMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Unlike adolescents with adolescent-onset (AO) disruptive behavior, adolescents with early-onset (EO) disruptive behavior may not benefit from treatment. METHOD: Using Symptom Checklist (SCL-90-R) ratings at admission and discharge of adolescent inpatients with EO (n = 85) and AO (n = 60) disruptive behavior treatment outcome was determined by (a) a change in mean scores and (b) the Reliable Change Index. For a subgroup, ratings on the Satisfaction Questionnaire Residential Youth Care for Parents (n = 83) were used to verify the treatment outcome. RESULTS: Inpatients with EO disruptive behavior had a higher risk of dropout (44.4%) from treatment than the AO group (24.7%). Among the treatment completers, both onset groups reported improvements on the SCL-90-R, with 26.9% recovering and 31.7% improving. Inpatients who reported improvement were mostly rated as improved by their parents (r = .33). CONCLUSION: As EO inpatients are more likely to drop out, interventions should aim at motivating youngsters to continue treatment, particularly given the poor outcome in this group. Treatment may benefit both groups because those EO youths who stayed in treatment improved to the same extent as AO inpatients.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.143
GPT teacher head0.465
Teacher spread0.322 · 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 designObservational
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

Citations3
Published2016
Admission routes1
Has abstractyes

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