Factorial structure and measurement invariance of the Italian version of the Cooper – Norcross Inventory of Preferences (C-NIP)
Bibliographic record
Abstract
Abstract There is increasing evidence that psychotherapy efficacy can be enhanced by accommodating clients’ preferences regarding their role, the treatment, and the therapist. Several instruments are available to measure these concepts, although only the Cooper-Norcross Inventory of Preferences (C-NIP) appears particularly suitable for psychotherapy. This study aimed to validate the Italian version of the C-NIP and to provide norms for both clinical and research use for Italian-speaking individuals. We adopted a multi-step procedure to translate the C-NIP into Italian. Then, 1084 (70.3% females; Mage = 27.22 ± 11 years) Italian adults completed an online survey. Psychometric properties of both the original C-NIP and a revised, 15-item, five-scale version of this questionnaire were analysed through a Confirmatory Factor Analysis (CFA), McDonald’s omega coefficients, mean inter-item correlations, measurement invariance, and Pearson correlations. The Italian translation of the original version of the C-NIP scales did not show good psychometric properties. However, the CFA on a revised factorial structure of the C-NIP evidenced adequate fit to the data. We found good support for the unidimensionality of all scales, but only one of the scales demonstrated an acceptable internal consistency. Measurement invariance was confirmed across both patient sex and across individuals who were and were not in psychotherapy. Results showed that the revised version of the C-NIP has satisfactory factorial structure for use with Italian adults. More research is needed to investigate how preferences vary over time and in relation to psychopathologies and client characteristics.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".