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Record W4379516450 · doi:10.1080/26929953.2023.2188500

Psychometric Properties of the Persian version of the Compulsive Sexual Behavior Disorder Scale (CSBD-19)

2023· article· en· W4379516450 on OpenAlexaff
Elnaz Khayer, Maryam Rad, Beáta Bőthe, Farnaz Farnam

Bibliographic record

VenueSexual Health & Compulsivity · 2023
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCronbach's alphaConstruct validityPersianPsychologyConfirmatory factor analysisValidityClinical psychologyFace validityContext (archaeology)Scale (ratio)Content validityReliability (semiconductor)PsychometricsStructural equation modelingStatistics

Abstract

fetched live from OpenAlex

Conducting studies in the field of Compulsive Sexual Behavior Disorder (CSBD) has mostly been limited to Western cultures, and disagreements regarding the definition and assessment of this disorder are still debated. This study aims to examine the validity and Reliability of the Persian version of the Compulsive Sexual Behavior Disorder Scale (CSBD-19). We conducted this cross-sectional online study from April to July 2022 among 200 Iranian women and men (100 women, Mage (SD)* = 38.5 years(12/79), 100 males, Mage (SD) = 48.5 years (16/26)). After back-to-back translations, we examined the Face, Content, and Construct Validity, Reliability, and Confirmatory Factor Analysis. The results indicated excellent Face validity (impact score >1.5 for all items), Content validity (CVI > 0.79), Reliability (Cronbach’s alpha ≥ 0.95, CI = 95% (0.92-0.98)). The pre-established five-factor model of the CSBD-19 (i.e., control, salience, relapse, dissatisfaction, and negative consequences) had an excellent fit to the data and demonstrated appropriate associations with the correlates. The Persian version of the CSBD-19 is a valid and reliable scale based on the ICD-11 definition of CSBD that may identify people at high risk for CSBD. This tool can apply in large sample size studies, and in a traditional context, such as Islamic countries. Mean (Standard Deviation)*

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 score0.082
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.071
GPT teacher head0.365
Teacher spread0.294 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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