MétaCan
Menu
Back to cohort
Record W2113421033 · doi:10.5539/ijps.v4n1p182

Comparison the Effects of Communication and Conflict Resolution Skills Training on Marital Satisfaction

2012· article· en· W2113421033 on OpenAlexvenueno aff
Mahin Askari, Sidek B. Mohd Noah, Siti Aishah Hassan, Maznah Baba

Bibliographic record

VenueInternational Journal of Psychological Studies · 2012
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyConflict resolutionTest (biology)Nonprobability samplingSocial psychologyCommunication skillsApplied psychologyClinical psychologyMedical educationDemographyMedicine

Abstract

fetched live from OpenAlex

The purpose of the study was to examine the effects of communication and conflict resolution skills training on marital satisfaction among Iranian couples based on PREPARE-ENRICH program. In this study, marital satisfaction was measured by ENRICH Marital Satisfaction. The methodology of this study was experimental method; with pre-test, post-test, and control group design. Purposive sampling was conducted to select the sample that was included 54 couples who were consisted of all couples referred to the researcher by counselling centres. The referrals were done for about two months in 2009. These couples were randomly assigned to an experimental and a control group as well. The dependent variables were marital satisfaction, and the independent variables were communication and conflict resolution skills training. Consequently, the results indicated that communication and conflict resolution skills training improved marital satisfaction (p<.05). Moreover, the results showed that communication and conflict resolution training was effective in martial satisfaction in post-test (p<.05). In conclusion the findings of this study indicated that the on PREPARE-ENRICH program can be effective in improving marital satisfaction among Iranian couples.

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 score0.103
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.121
GPT teacher head0.512
Teacher spread0.390 · 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

Citations53
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Psychological StudiesSame topicAttachment and Relationship DynamicsFrench-language works237,207