Dutch translation and cross-cultural adaptation of the LIMB-Q Kids questionnaire
Bibliographic record
Abstract
BACKGROUND: We aimed to translate and culturally adapt the LIMB-Q Kids questionnaire for use in the Netherlands. The LIMB-Q Kids is a patient-reported outcome measure designed to assess functional, psychosocial, and aesthetic aspects of living with a limb difference in paediatric populations. AIM: To investigate the feasibility of a questionnaire in the Netherlands, which was translated into Dutch after having already been successfully translated and validated in several other languages. METHODS: The translation and adaptation process followed best practice guidelines, including forward and backward translation, expert panel review, and cognitive debriefing interviews with patients. The interviews focused on the clarity and comprehensibility of the instructions, response options, and questionnaire items. RESULTS: The rigorous process resulted in a linguistically and conceptually equivalent Dutch version of the LIMB-Q Kids questionnaire. While some challenges were encountered, no major difficulties were reported. The constructs and cultural relevance were found to be relatable to the Dutch context. Minor adjustments were made based on patient feedback, such as clarifying questions and modifying translations for technical terms. CONCLUSION: We demonstrated the successful translation and cultural adaptation of the LIMB-Q Kids questionnaire for use in the Netherlands. By following best practices, the researchers have developed a version that is conceptually and linguistically equivalent to the original English version. The availability of this Dutch version will facilitate the assessment of outcomes in paediatric populations with limb differences, and potentially enable cross-cultural comparisons.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".