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Record W4321103892 · doi:10.3390/ijerph20043446

Effectiveness of Group Parent-Child Interaction Therapy on Problem Behaviors in Chinese Kindergartners

2023· article· en· W4321103892 on OpenAlexaff
Juanjuan Sun, Mowei Liu, Xiaoyun Li, Yuena Zhou, Yan Li

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2023
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsTrent University
FundersScience Foundation of Ministry of Education of ChinaShanghai Normal UniversityMinistry of Education of the People's Republic of China
KeywordsPsychologyDevelopmental psychologyGroup (periodic table)Physics

Abstract

fetched live from OpenAlex

Problem behaviors in early childhood may put young children at risk for negative behavioral and psychosocial problems. This study examined the effectiveness of group PCIT on Chinese young children’s externalizing and internalizing problems. The participants were 58 mothers with their children aged 2–3 years (M = 2.95 years, SD = 0.22), assigned to an immediate treatment (n = 26) group or to a waitlist (n = 32) control group. The program involved comprehensive group intervention and featured weekly 60–90-min sessions, totaling ten sessions over three months. Results indicate that group PCIT not only significantly improved teacher-reported problem behaviors in children, but also improved observed maternal parenting behavior. These findings support the use of group PCIT in Chinese children and provide mothers with an evidence-based tool to address problem behaviors in a non-clinical population.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.072
GPT teacher head0.430
Teacher spread0.358 · 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 designNon-randomized trial
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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

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