Developing a Delphi-Consensus Evaluation Framework for Clinical Research Training: A Chinese Model With Global Implications

构建临床研究培训的德尔菲共识评价框架:一个具有全球意义的中国模式

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Abstract

INTRODUCTION: Effective clinical research training is crucial for advancing medical science and improving patient care. However, current evaluation systems in China often focus on theoretical knowledge, neglecting practical skills and innovation. This study aimed to develop a comprehensive evaluation framework for clinical research training programs using the Delphi consensus method. METHOD: A 2-round Delphi method was employed, involving healthcare professionals and educators from top tertiary hospitals and leading academic institutions in China. The first round included 15 participants, and the second round included 19 participants. The evaluation framework was based on the Kirkpatrick model, covering Reaction, Learning, Behavior, and Results dimensions. Indicators were evaluated using a 5-point Likert scale, with consensus defined as a mean significance score ≥3.50 and a coefficient of variation ≤0.25. RESULTS: In the first round, 9 indicators were excluded and 5 added. In the second round, 26 indicators met consensus criteria. Key indicators included "Relevance of training content" (mean = 4.89, CoV = 0.06), "Degree of knowledge mastery" (mean = 4.58, CoV = 0.13), and "Impact on career development" (mean = 4.53, CoV = 0.15). Other significant indicators were "Timeliness of training information" (mean = 4.84, CoV = 0.08) and "Success rate of applying for scientific research funds" (mean = 4.05, CoV = 0.21). DISCUSSION: This study developed a comprehensive evaluation framework for clinical research training in China, emphasizing the importance of relevant training content, strong learning outcomes, and long-term professional impact. This framework provides a robust tool to assess and enhance clinical research training programs, ultimately contributing to improved healthcare and medical research. Future work should focus on validating this framework through empirical studies and refining it based on ongoing feedback.

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