Factorial validity and comparability of the six translations of the Rivermead Post-Concussion Symptoms Questionnaire translations: results from the CENTER-TBI study
Bibliographic record
Abstract
Abstract Background Comparison of patient-reported outcomes in multilingual studies requires evidence of the equivalence of translated versions of the questionnaires. The present study examines the factorial validity and comparability of six language versions of the Rivermead Post-Concussion Symptoms Questionnaire (RPQ) administered to individuals following traumatic brain injury (TBI) in the Collaborative European NeuroTrauma Effectiveness Research (CENTER-TBI) study. Methods Six competing RPQ models were estimated using data from Dutch (n = 597), English (n = 223), Finnish (n = 213), Italian (n = 268), Norwegian (n = 263), and Spanish (n = 254) language samples recruited six months after injury. To determine whether the same latent construct was measured by the best-fitting model across languages and TBI severity groups (mild/moderate vs. severe), measurement invariance (MI) was tested using a confirmatory factor analysis framework. Results The results did not indicate a violation of the MI assumption. The six RPQ translations were largely comparable across languages and were able to capture the same construct across TBI severity groups. The three-factor solution comprising emotional, cognitive, and somatic factors provided the best fit with the following indices for the total sample: χ 2 (101) = 647.04, $${\chi }^{2}/df$$ χ 2 / d f = 6.41, p < 0.001, CFI = 0.995, TLI = 0.994, RMSEA = 0.055, CI 90% [0.051, 0.059], SRMR = 0.051. Conclusion The RPQ can be used in international research and clinical settings, allowing direct comparisons of scores across languages analyzed within the full spectrum of TBI severity. To strengthen the aggregated applicability across languages, further analyses of the utility of the response scale and comparisons between different translations of the RPQ at the item level are recommended.
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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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".