Cross-cultural adaptation and validation of the Hebrew version of the Injustice Experience Questionnaire – long and short versions
Bibliographic record
Abstract
PURPOSE: To translate, validate, and culturally adapt the Injustice Experience Questionnaire (IEQ) and IEQ Short Form (IEQ-SF) into Hebrew, as measuring tools for examining feelings of injustice in cases of accidents and chronic pain. METHODS: The translation was performed in several steps following the cross-cultural adaptation process. A sample of 150 patients suffering from traumatic injury fill out a battery of questionnaires: IEQ, IEQ-SF, Hospital Anxiety and Depression Scale (HADS), Numeric Pain Rating Scale (NPRS), and Pain Catastrophizing Scale (PCS), which were used for calculating construct validity. A test-retest was performed on 41 patients. RESULTS: The IEQ and IEQ-SF found Cronbach's alpha of 0.92 and 0.84, respectively. Test-retest reliability for IEQ (ICC: 0.94) was found to be excellent. Spearman's correlation coefficient between IEQ and PCS was 0.68, NPRS (severe pain: 0.45, average pain: 0.51), HADS (anxiety: 0.62, depression: 0.60). The correlation between IEQ-SF and PCS was 0.67, with HADS (anxiety: 0.52, depression: 0.48). A weak correlation was found for NPRS (severe pain: 0.30, average pain: 0.34). CONCLUSIONS: The Israeli translation and cross-cultural adaptation of the IEQ and IEQ-SF questionnaires were found to be valid and reliable.Implications for rehabilitationThe perception of injustice is a significant mental and psychological factor for recovery after accidents and injuries.This study translated, validated and culturally adapted the Injustice Experience Questionnaire (IEQ) and the short form into Hebrew.The questionnaires were found to be valid and reliable in Hebrew.
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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 | 0.008 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".