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Department of Rehabilitation Medicine

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Recommendations for translation and reliability testing of international spinal cord injury data sets


Biering-Sorensen F, Alexander MS, Burns S, Charlifue S, DeVivo M, Dietz V, Krassioukov A, Marino R, Noonan V, Post MW, Stripling T, Vogel L, Wing P; Executive Committee for the International SCI Standards and Data Sets



Publication Info:

Spinal Cord, 49(3):357-360


OBJECTIVE: To provide recommendations regarding translation and reliability testing of International Spinal Cord Injury (SCI) Data Sets.
SETTING: The Executive Committee for the International SCI Standards and Data Sets.
RECOMMENDATIONS: Translations of any specific International SCI Data Set can be accomplished by translation from the English version into the target language, and be followed by a back-translation into English, to confirm that the original meaning has been preserved. Another approach is to have the initial translation performed by translators who have knowledge of SCI, and afterwards controlled by other person(s) with the same kind of knowledge. The translation process includes both language translation and cultural adaptation, and therefore shall not be made word for word, but will strive to include conceptual equivalence. At a minimum, the inter-rater reliability should be tested by no less than two independent observers, and preferably in multiple countries. Translations must include information on the name, role and background of everyone involved in the translation process, and shall be dated and noted with a version number.
CONCLUSION: By following the proposed guidelines, translated data sets should assure comparability of data acquisition across countries and cultures. If the translation process identifies irregularities or misrepresentation in either the original English version or the target language, the working group for the particular International SCI Data Set shall revise the data set accordingly, which may include re-wording of the original English version in order to accomplish a compromise in the content of the data set.

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