Translating Instruments Into Other Languages: Development and Testing Processes
Bibliographic record
Abstract
Cross-cultural influences affect perceptions and health practices, which are 2 areas of nursing concern. Culturally sensitive assessment instruments are needed, but many challenges exist in obtaining valid and reliable measurement. Translating questionnaires for cross-cultural research is fraught with methodological pitfalls related to colloquial phrases, jargon, idiomatic expressions, word clarity, and word meanings. It cannot be assumed that a particular concept has the same relevance across cultures. Simply translating an English version word-for-word into another language is not adequate to account for linguistic and cultural differences. Ideally, the perspectives of people from the culture about the concept of interest should be studied first, but often a practical alternative is to find and translate a tool developed in another culture. The purpose of this article is to describe important considerations in conducting translation for equivalence, types of equivalence, and strategies to translate instruments that promote equivalence and how to test the translated version for equivalence. These concepts and strategies are illustrated by describing the translation process of Hilton's Uncertainty Stress Scale into French and the use and testing of the French version with a French Canadian sample in Skrutkowski's study of perceived uncertainty in adult survivors of cancer.
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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 | 0.250 | 0.366 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".