One Word, Many Faces: The Italian Validation of the Multiscale Dissociation Inventory
Bibliographic record
Abstract
This study aimed to validate the Italian version of the Multiscale Dissociation Inventory (MDI-ITA) and to assess its psychometric properties in a non-clinical sample. The MDI was translated and culturally adapted following established guidelines. A total of 439 Italian-speaking adults participated in the study and filled out the MDI-ITA and other assessment tools for dissociation depression, anxiety, stress, and post-traumatic symptoms. The internal consistency, test-retest reliability, and construct validity of the MDI-ITA were evaluated using exploratory and confirmatory factor analyses. Consistent with previous validation studies, the analyses supported a five-factor structure: Disengagement, Depersonalization/Derealization, Emotional Constriction, Memory Disturbance, and Identity Dissociation. Cronbach's alpha values for each subscale were all greater than 0.71, demonstrating satisfactory internal consistency. Test-retest reliability was also high, with a correlation of 0.91. Convergent validity was supported by significant positive correlations between MDI-ITA and other dissociation measures. Discriminant validity was indicated by weaker correlations with depression, anxiety, and post-traumatic symptoms. The MDI-ITA is a reliable and valid tool for assessing dissociative phenomena in Italian-speaking populations with potential applications in both clinical and research settings. Its multidimensional structure offers comprehensive insights into dissociation, facilitating the development of targeted interventions for individuals presenting with these symptoms.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".