Hendrix Dean
Education Services, Health Sciences Library, State University of New York at Buffalo, Buffalo, NY 14214, USA.
J Med Libr Assoc. 2008 Oct;96(4):324-34. doi: 10.3163/1536-5050.96.4.007.
The objective of this study was to analyze bibliometric data from ISI, National Institutes of Health (NIH)-funding data, and faculty size information for Association of American Medical Colleges (AAMC) member schools during 1997 to 2007 to assess research productivity and impact.
This study gathered and synthesized 10 metrics for almost all AAMC medical schools(n=123): (1) total number of published articles per medical school, (2) total number of citations to published articles per medical school, (3) average number of citations per article, (4) institutional impact indices, (5) institutional percentages of articles with zero citations, (6) annual average number of faculty per medical school, (7) total amount of NIH funding per medical school, (8) average amount of NIH grant money awarded per faculty member, (9) average number of articles per faculty member, and (10)average number of citations per faculty member. Using principal components analysis, the author calculated the relationships between measures, if they existed.
Principal components analysis revealed 3 major clusters of variables that accounted for 91% of the total variance: (1) institutional research productivity, (2) research influence or impact, and (3)individual faculty research productivity. Depending on the variables in each cluster, medical school research may be appropriately evaluated in a more nuanced way. Significant correlations exist between extracted factors, indicating an interrelatedness of all variables. Total NIH funding may relate more strongly to the quality of the research than the quantity of the research. The elimination of medical schools with outliers in 1 or more indicators (n=20)altered the analysis considerably.
Though popular, ordinal rankings cannot adequately describe the multidimensional nature of a medical school's research productivity and impact. This study provides statistics that can be used in conjunction with other sound methodologies to provide a more authentic view of a medical school's research. The large variance of the collected data suggests that refining bibliometric data by discipline, peer groups, or journal information may provide a more precise assessment.
本研究旨在分析1997年至2007年期间美国医学协会（AAMC）成员学校的ISI文献计量数据、美国国立卫生研究院（NIH）资助数据以及教师规模信息，以评估研究生产力和影响力。
本研究收集并综合了几乎所有AAMC医学院校（n = 123）的10项指标：（1）每所医学院发表文章的总数，（2）每所医学院发表文章的总被引次数，（3）每篇文章的平均被引次数，（4）机构影响指数，（5）零被引文章的机构百分比，（6）每所医学院的年平均教师人数，（7）每所医学院的NIH资助总额，（8）每位教师获得的NIH资助平均金额，（9）每位教师的平均文章数，以及（10）每位教师的平均被引次数。作者使用主成分分析来计算各项指标之间的关系（如果存在的话）。
主成分分析揭示了3个主要的变量集群，它们占总方差的91%：（1）机构研究生产力，（2）研究影响力，以及（3）个体教师研究生产力。根据每个集群中的变量，可以以更细致入微的方式对医学院校的研究进行适当评估。提取的因素之间存在显著相关性，表明所有变量之间存在相互关联性。NIH资助总额与研究质量的关联可能比与研究数量的关联更强。剔除1个或多个指标存在异常值的医学院校（n = 20）后，分析结果有很大变化。
尽管序数排名很流行，但它无法充分描述医学院校研究生产力和影响力的多维度性质。本研究提供的统计数据可与其他合理方法结合使用，以更真实地呈现医学院校的研究情况。收集数据的巨大差异表明，按学科、同行群体或期刊信息细化文献计量数据可能会提供更精确的评估。
