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Record W4313060671 · doi:10.52547/rbs.19.3.439

Analysis of structural equations of emotional distress based on family emotional atmosphere and attachment styles mediated by differentiation and body image of married people

2021· article· en· W4313060671 on OpenAlexaboutno aff
Shirin Shahmardi, Taghi Pourebrahim, Mohammad Bagher Hobbi

Bibliographic record

VenueJournal of Research in Behavioural Sciences · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsnot available
Fundersnot available
KeywordsAtmosphere (unit)Emotional distressPsychologyDistressStructural equation modelingDevelopmental psychologySocial psychologyClinical psychologyAnxietyComputer scienceGeographyPsychiatry

Abstract

fetched live from OpenAlex

Aim and Background: Research has shown that emotional distress can be affected by body image and family circumstances. Therefore; the aim of this study was to analyze the structural equations of emotional malaise based on family emotional atmosphere and attachment styles mediated by differentiation and body image. Methods and Materials: The research method is descriptive-correlation of structural equations. The statistical population consisted of all married men and women living in District 2 of Tehran in 1399. The sample size was estimated to be 400 using the method proposed by Schumacher and Lomax (2004). Sampling method was available and online. Research tools included questionnaires including Toronto Mood Disorder (1994), Hillburn Family Emotional Atmosphere (1964), Collins and Reed Attachment Styles (1990), Scorne and Friedlander (1988) Differentiation, and Director and Garcia (2002) Body Image. They were. To analyze the data, structural equation modeling technique was used in AMOS software environment.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.027
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.100
GPT teacher head0.486
Teacher spread0.386 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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