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Record W2581806621 · doi:10.4236/psych.2017.81012

Psychometric Properties of the Persian Version of the Vancouver Obsessional-Compulsive Inventory Inventory (VOCI) in Iranian Non-Clinical Sample

2017· article· en· W2581806621 on OpenAlexaboutno aff
Habibollah Ghassemzadeh, Giti Shams, Ali Pasha Meysami, Narges Karamghadiri

Bibliographic record

VenuePsychology · 2017
Typearticle
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsPersianPsychologyExploratory factor analysisConvergent validityReliability (semiconductor)Clinical psychologyInternal consistencyPsychiatryPsychometricsTheology

Abstract

fetched live from OpenAlex

The Vancouver Obsessional-Compulsive Inventory (VOCI) is a self-report inventory developed to assess a wide range of Obsessive-Compulsive symptoms. The aim of this study was to investigate psychometric properties of the Persian version of the VOCI in non-clinical samples. A questionnaire package including the VOCI, BDI-II, BAI, MOCI, OCI-R and PSWQ was administered to volunteer undergraduate students (n = 233, 139 females, 94 males) from two Iran universities (Tehran University of Medical Sciences and Allameh Tabatabaei University). All the assessments were repeated in the same sample after 2 weeks. Psychometric analyses were run to assess reliability and validity of the Persian version of the VOCI. We converged an exploratory factor analysis to test the factor structure. The VOCI-Persian had good internal consistency, test-retest reliability, convergent and divergent validity. The present study showed that the factor structure of the questionnaire consisted of five main factors: VOCI Contamination, Checking, Obsessions, Hoarding and Perfectionism/Indecisiveness. Further studies are needed to develop psychometric tools with stronger diagnostic performance for OCD assessment.

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)0.001
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 score0.036
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
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 head0.054
GPT teacher head0.357
Teacher spread0.303 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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