Psychometric Properties and Network Analysis of the Arabic Version of Reinforcement Sensitivity Theory of Personality Scale-Short Version in Patients with Anxiety Disorders
Bibliographic record
Abstract
This study aimed to examine the psychometric properties of the Arabic version of a short version of the Reinforcement Sensitivity Theory of Personality Questionnaire (RST-PQ-S) among a sample of 700 patients with anxiety disorders (53.1% were female). Participants completed the RST-PQ-S, NEO-FFI, Positive Mental Health (PMH), and Kessler Psychological Distress scale. Both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were employed to test the construct validity of the scale. This study also utilized a network perspective, incorporating Exploratory Graph Analysis (EGA) and centrality measures. As a result of the EFA and CFA, it was determined that the scale consists of 22 items and six subdimensions. These subdimensions were named as follows: "Flight Fight-Freeze System", "Behavioral Inhibition System", "Reward Interest", "Reward Reactivity", "Goal-Drive Persistence", and "Impulsivity". Additionally, the network analysis's findings confirmed the six-factor structure derived from the construct validity assessment. The results of this study demonstrated that the Arabic version of the personality scale is a valid and reliable tool for assessing personality in Arabic-speaking individuals with anxiety disorders. It has the potential to serve as an important diagnostic instrument in clinical and research settings. These findings may assist psychologists and clinicians in Arabic-speaking countries to better understand how the personality traits and anxiety disorders are related.
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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 | 0.010 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".