Translation and cross-cultural adaptation of the National Health Service Sustainability Model to the Chinese healthcare context

Background International attention is being paid to the issue of making evidence sustainable after implementation. Developing an identification model is essential to promote and monitor the sustainability of evidence implementation. However, this model is not available in Chinese. This study aims to translate the National Health Service Sustainability Model into Chinese and to verify whether the model is adapted to the Chinese healthcare environment. Methods This study follows the translation and validation guidelines developed by Sousa and Rojjanasrirat. The translations include forward and backward translations and their comparison. Expert reviews were used to validate the content validity of the Chinese version of the National Health Service sustainability model. Cognitive interviews were used to assess the validity of the language in the Chinese setting. Results The translation was conducted by a bilingual research team and took 12 months. Expert reviews were undertaken with eight experts, and cognitive interviews with six participants. The content validity of the model is excellent, but at least 20% of the experts still felt that items one, three, five and nine needed refinements. In the cognitive interviews, most items, instructions and response options were well understood by the participants responsible for the evidence-based practice project. However, some language issues were still identified in items one, three, four, five, seven, nine, and ten. Participants reported that the sustainability results of the model assessment were consistent with their previous judgments of the items. Based on the expert review and interview results, items one, three, four, five, seven, nine and ten require further refinement. In summary, seven of the ten items have been amended. Conclusions This study provides insight into how the National Health Service sustainability model can be used in the Chinese healthcare setting and paves the way for future large-scale psychometric testing.