A checklist for translating and adapting questionnaires (CTAQ) in healthcare research: insights from a Delphi method approach
Bibliographic record
Abstract
PURPOSE: Accurate translation and adaptation of survey questionnaires are essential for ensuring validity and reliability in cross-cultural healthcare research. Despite the global expansion of healthcare studies, standardized guidelines for the translation process are limited. METHODS: To address this gap, we developed the Checklist for Translating and Adapting Questionnaires (CTAQ). A three-round Delphi survey was conducted to refine and validate the CTAQ. An international panel of experts in survey methodology, cross-cultural research, and healthcare participated in the study, providing iterative feedback to achieve consensus on checklist items. The development of the CTAQ involved: (i) drafting an initial checklist based on a comprehensive literature review and expert insights; (ii) rating the importance and relevance of each item using an 80% consensus threshold; and (iii) revising items through successive Delphi rounds until consensus was reached. RESULTS: The finalized CTAQ comprises eight stages: defining the target audience and objectives; forming a translation team; forward and backward translation; comparing versions; reconciliation; pretesting and evaluation; final review and proofreading; and post-survey evaluation. This structured approach, informed by expert consensus, integrates best practices and addresses cultural nuances, thereby enhancing the accuracy and reliability of translated survey instruments. CONCLUSIONS: The CTAQ offers a systematic, consensus-based framework that enhances the linguistic and cultural accuracy of translated survey instruments in healthcare research. PRACTICE IMPLICATIONS: Adopting the CTAQ standardizes translation workflows and promotes the production of valid, reliable, and culturally appropriate questionnaires. This contributes to greater rigor and quality in international and cross-cultural healthcare studies.
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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 | 0.403 | 0.446 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".