ObjectiveTo develop a survey questionnaire on preferences and values regarding perineal injury prevention measures during pregnancy and conduct reliability and validity tests. MethodsCombining literature reviews, qualitative interviews, and expert consultations, we summarized key elements of perineal injury prevention during pregnancy and synthesized the best evidence. Through multiple discussions within the core working group, a survey questionnaire on preferences and values regarding perineal injury prevention measures during pregnancy was formulated. Using convenience sampling, pregnant women were recruited, and a pre-survey was conducted using the questionnaire. Pre-survey results were analyzed using item analysis and reliability and validity testing methods to validate and refine the questionnaire. ResultsThe questionnaire was compiled based on the theory of evidence-based decision-making. The initial version of the questionnaire was developed by combining systematic evaluation, network meta-analysis, and semi-structured interviews. The questionnaire was modified and improved through expert consultation, group discussion, and pre-investigation, which ensured that the questionnaire had good reliability, validity, and practicability. The Cronbach's α coefficient was 0.87, the split-half reliability was 0.71, and the content validity index was 0.97 of the survey questionnaire. ConclusionThe present version of the perineal injury preventive measures preference and values questionnaire has good reliability, validity, and practicability. It can serve as a valuable tool for investigating preferences and values related to perineal injury prevention during pregnancy.
In meta-analysis, heterogeneity in statistical measures across primary studies can significantly affect the efficiency of data synthesis and the accuracy of result interpretation. Such inconsistencies may introduce bias in effect size estimation and increase the complexity of pooled analyses. Therefore, establishing standardized approaches for data type transformation and harmonizing different statistical measures has become a critical step in ensuring the quality of meta-analyses. To achieve efficient and scientifically rigorous data integration, researchers need to master systematic data transformation techniques and develop standardized processing strategies. Based on this need, this study provides a comprehensive summary of effect size transformation methods in meta-analysis, focusing on standardizing binary and continuous variables. It offers practical guidance to support researchers in applying these methods consistently and accurately.
The overlap of literature in umbrella reviews can affect the reliability and accuracy of research conclusions, leading to results with a higher risk of bias. Therefore, it becomes crucial to assess the degree of overlapping and how to handle it. In order to avoid redundant calculations and reduce the risk of bias, researchers need to quantify the degree of literature overlap and adopt corresponding processing strategies. This paper provides a detailed introduction to the calculation methods of overlapping and different strategies for handling overlapping, aiming to provide a reference and guidance for domestic scholars' understanding and application of this method.
