Transcultural adaptation of the Injustice Experience Questionnaire into Brazilian Portuguese
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: It has been proposed that some individuals with musculoskeletal pain may perceive themselves as victims of injustice. Perception of injustice can have a significant impact on several pain-related outcomes, major depressive symptoms, disabilities, and absenteeism. The objective of this study consisted of a transcultural adaptation of the original instrument in English, Injustice Experience Questionnaire into a final version to be used in Brazil. METHODS: The whole translation process consisted of translation, back-translation and the review by a committee of experts. The pre-test was applied to 90 participants (41 participants with chronic musculoskeletal pain). For the psychometric analysis, the translated version was applied to 120 participants with chronic musculoskeletal pain. The internal consistency was verified by the Cronbach’s alpha coefficient, and the construct validity was analyzed using factorial exploratory analysis. RESULTS: After the conclusion of the pre-test, there were no difficulties in understanding the translated questionnaire by more than 20% of the sample. The Cronbach alpha calculation for the 12 items of the Injustice Experience Questionnaire/Port-BR was 0.86 [CI (95%) = 0.83 to 0.90; p<0.001]. CONCLUSION: The questionnaire’s Portuguese version proved to be easily understandable showing good semantic validation. Nevertheless, further studies should address other psychometric characteristics of this instrument.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".