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Record W2599941989 · doi:10.3138/jcfs.44.1.41

The Role of Family Typology on Mental Health, Positive and Negative Emotions, Self-esteem, and Wife Physical Abuse in an Iranian Sample

2013· article· en· W2599941989 on OpenAlexvenueno aff
Siamak Khodarahimi

Bibliographic record

VenueJournal of Comparative Family Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsnot available
Fundersnot available
KeywordsTypologyPsychologyMental healthWifeSelf-esteemPsychopathologyPhysical abuseClinical psychologyPsychological abusePoison controlDevelopmental psychologySuicide preventionDomestic violencePsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the roles of the cohesive, enmeshed and disengaged families on the mental health, positive and negative emotions, self-esteem and wife abuse in married couples, to investigate relationships between these constructs, and to explore the effects of gender and the level of education on aforesaid variables. Participants included 100 couples that randomly selected from Shiraz city, Iran. A demographic questionnaire and four selfrating measures were used in this study. Resulting data indicated that cohesive families had a significantly lower psychopathology, negative emotions and the wi fc physical abuse and a higher positive emotions and self-esteem than both enmeshed and disengaged families. Psychopathology factors were positively correlated to negative emotions and wife physical abuse, and they had significantly negative relationships with positive emotions and self-esteem. Results were confirmed the effects of family typology on the mental health. Negative emotions and self-esteem were predicted the wife physical abuse in this sample.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.375
Teacher spread0.320 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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