Psychometric Properties of the Persian Version of the Revised Temperament and Character Inventory (TCI-140) in a Psychiatric Outpatient Sample in Iran
Bibliographic record
Abstract
Background: Cloninger first proposed the personality theory, considering both normal and abnormal personality traits. Later, different complementary versions of the Temperament and Character Inventory (TCI) found their way into the academic milieu to enhance their psychometric properties and efficiency in both experimental and clinical settings. Objectives: The main objective of the current research was to investigate the principal psychometric properties of the Persian version of the Temperament and Character Inventory (TCI-140). Methods: This research is a cross-sectional study. The data included information on psychiatric outpatients visiting Roozbeh psychiatric hospital in 9 months in 2021. Purposive sampling was performed on volunteers. A total of 471 outpatients filled out the TCI-140, 150 of whom also filled out the Personality Inventory for the DSM-5 (PID-5). Exploratory factor analysis (EFA) was performed using principal component analysis (PCA) by Promax rotation. Results: The internal consistency of all dimensions (Cronbach's alpha: Above 0.70, except for reward dependence) was proved to be satisfactory, but that of some subscales (NS1, NS4, RD4, CO3, and CO5) was quite poor. Test-retest reliability confirmed that for all dimensions, ICC > 0.70, indicating a high reliability. The findings of the PCA revealed that all dimensions were loaded in accordance with the theoretical expectations. At the facet level, all the facets were loaded on their factors except for sentimentality and dependence. According to the correlation findings, the concurrent validity of TCI-140 was acceptable for PID-5. The results showed that HA had relatively high positive correlations with detachment (r = 0.55) and negative affect (r = 0.48). Conclusions: The results confirmed the satisfactory reliability and validity of the Persian version of TCI-140 despite its drawbacks. Hence, it can be employed to examine personality traits.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".