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Effectiveness of psychosocial intervention for children and adolescents with comorbid problems: a systematic review

2011· review· en· W1933237798 on OpenAlexaff
Priscilla Burnham Riosa, Brae Anne McArthur, Michèle Preyde

Bibliographic record

VenueChild and Adolescent Mental Health · 2011
Typereview
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPsychosocialIntervention (counseling)Systematic reviewPsychologyMEDLINEClinical psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Comorbidity is common among child clinical samples. Reviews on effective intervention for comorbid problems are lacking. METHOD: Based on a literature search of three databases (PsycINFO, MEDLINE and ERIC), initial data analysis was carried out on 865 studies; of these,10 randomised trials fully met study inclusion criteria and were subject to final analysis, with quality assessments and effect sizes calculated. RESULTS: Overall, effect sizes for externalising (M = 1.12) and internalising (M = 1.09) outcomes were large. Effect sizes were large for family-based (M = 1.80) compared to individual (M = 0.78) and group-based (M = 0.54) interventions. Studies with homotypic comorbidity (M= 1.18) displayed larger treatment effect sizes than ones with heterotypic comorbidity (M = 0.54). CONCLUSIONS: While the overall quality ratings of the reviewed studies varied from mediocre to good, with a variety of measures used across studies to assess the same outcomes, findings suggest that current interventions are effective for reducing internalising and externalising problems in children with comorbidity. More substantive evidence for the beneficial effects of psychosocial interventions for children with comorbid problems may arise as more robust studies, which more explicitly address and describe comorbidity, become available.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.077
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.027
GPT teacher head0.338
Teacher spread0.310 · 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 designSystematic review
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

Citations32
Published2011
Admission routes1
Has abstractyes

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