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Record W3008846075 · doi:10.1002/jclp.22946

Collaborative Problem Solving reduces children's emotional and behavioral difficulties and parenting stress: Two key mechanisms

2020· article· en· W3008846075 on OpenAlexaff
Georgina Heath, Chris Fife‐Schaw, Lu Wang, Christopher J. Eddy, Michael Hone, Alisha R. Pollastri

Bibliographic record

VenueJournal of Clinical Psychology · 2020
Typearticle
Languageen
FieldHealth Professions
TopicProblem Solving Skills Development
Canadian institutionsMental Health Research Canada
Fundersnot available
KeywordsEmpathyPsychologyFidelityIntervention (counseling)Developmental psychologyAdaptive functioningClinical psychologyExecutive functionsCognitionPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: This study aimed to evaluate the effectiveness of the Collaborative Problem Solving (CPS) approach in home-based family therapy and to explore two hypothesized mechanisms of change. METHOD: Sixty-seven families with children aged 3-12 years old completed a 12-week home-based CPS treatment program. Parent-report measures were completed pre- and post-intervention, including measures on parents' fidelity of using CPS, parents' empathy, children's executive functioning, children's behavioral difficulties, and parenting stress. RESULTS: There were significant reductions in children's behavioral difficulties and parenting stress, and significant improvements in children's executive functioning and parents' empathy. These improvements were greatest for parents who had the greatest fidelity to CPS. Improvements in children's executive functioning and parents' empathy mediated the relationship between parents' CPS fidelity and outcomes. CONCLUSIONS: These results provide evidence that home-based family treatment with CPS may achieve positive child and family outcomes by building children's executive function skills and improving parents' empathy.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.130
GPT teacher head0.511
Teacher spread0.381 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2020
Admission routes1
Has abstractyes

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