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Record W4396530320 · doi:10.61838/kman.jprfc.1.2.5

Improving Family Functioning with Social Skills Training: Evidence from a Quasi-Experimental Study

2023· article· en· W4396530320 on OpenAlexaff
Nadereh Saadati, Zahra Yousefi

Bibliographic record

VenueJournal of Psychosociological Research in Family and Culture · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyTraining (meteorology)Social skillsDevelopmental psychologyApplied psychologyClinical psychology

Abstract

fetched live from OpenAlex

Objective: To examine the effectiveness of social skills training on enhancing family functioning among married individuals. Methods and Materials: Employing a quasi-experimental design, this study involved 30 married individuals visiting a private counseling center, divided equally into experimental and control groups. The experimental group received an 8-week social skills training program focused on improving communication, problem-solving, and emotional regulation within marital relationships. Findings: Results indicated significant improvements in family functioning scores for the experimental group, as compared to the control group, both immediately following the intervention and at a 3-month follow-up. The analysis of variance with repeated measurements underscored the positive impact of social skills training on enhancing family dynamics. Conclusion: Social skills training emerges as a viable intervention for improving family functioning among married couples, suggesting its potential inclusion in marital counseling services to foster healthier family relationships.

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)0.011
metaresearch head score (Gemma)0.018
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: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.219
GPT teacher head0.450
Teacher spread0.231 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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