Second-Order Factor Structure of the Vancouver Obsessive Compulsive Inventory (VOCI) in a Non-Clinical Sample
Bibliographic record
Abstract
BACKGROUND: The Vancouver Obsessive Compulsive Inventory (VOCI) is a self-report measure of the severity of obsessive-compulsive problems such as contamination, checking, obsessions, hoarding, needing things to be just right, and indecisiveness. In the seminal paper a six-correlated-factor structure was found in a sample of OC patients, but the issue of the factor structure of the VOCI in non-clinical populations was not addressed. AIM: This study assesses the psychometric properties and the factor structure of the Italian version of the VOCI in a non-clinical sample. METHOD: The VOCI was administered to a large community sample (n = 445). Some participants also completed a battery including measures of OC behaviour, worry, anxiety and depression (n = 89) and were administered the VOCI twice at an 8-week interval (n = 46). RESULTS: Confirmatory factor analyses replicated the six-correlated-factor structure originally found in a patient sample, but a more parsimonious, second-order-factor model showed a statistically higher fit, suggesting that VOCI subscales can be considered as facets of a higher-order OCD factor. The whole item pool and each of the subscales showed good internal consistency, unidimensionality, test-retest reliability and convergent construct validity. As in the original version, limited support for discriminant validity was found. Scores were weakly associated with age, gender and education. CONCLUSIONS: Although some key issues still need to be investigated (e.g. sensitivity to change), the VOCI seems to be a psychometrically sound instrument for the assessment of OCD-related behaviours and thoughts and can be used in cultural contexts different from the original.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".