Psychometric Evaluation of the Persian Version of the Traumatic Injuries Distress Scale
Bibliographic record
Abstract
Purpose: The traumatic injuries distress scale (TIDS) is a tool to assess acute emotional distress after post-musculoskeletal injuries. The purpose of this study was to evaluate the psychometric properties of the Persian version of the TIDS (TIDS-P). Methods: Participants (n = 100, mean age = 32.5, 82% male) with acute musculoskeletal injuries of any etiology completed the TIDS-P and the Persian version of the Brief Pain Inventory (BPI-P) on a single occasion, with 15 completing a re-test in seven days. Structural validity (confirmatory factor analysis), criterion validity (Spearman's rho), internal consistency (Cronbach's alpha), and test-retest reliability (intra-class correlation coefficient, ICC2, 1) were assessed. Results: TIDS-P demonstrated excellent criterion validity as the correlation values were similar to the English version (r = 0.73, 0.56 versus 0.73 and 0.47, respectively). Adequate statistical criteria were demonstrated for the three-factor structure of TIDS-P (X2 = 88.15, df = 51, P < 0.001, CFI = 0.95, TLI = 0.96, and RMSEA = 0.086). The internal consistency was acceptable with Cronbach's alpha of 0.61 for the hyperarousal/intrusion subscale, 0.83 for the negative affect subscale, and 0.78 for the uncontrolled pain subscale. The ICC2, 1 values demonstrated excellent test-retest reliability (0.92). Conclusion: Evaluation of the psychometric properties of the TIDS-P provide excellent reliability and appropriate structural validity for assessment of emotional distress post-musculoskeletal injuries in Persian populations.
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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 | 0.016 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".