There has been a proliferation of descriptive for COVID-19 papers using altmetrics. The main objective of this study is to analyse whether the altmetric mentions of COVID-19 medical studies are associated with the type of study and its level of evidence. Data were collected from PubMed and Altmetric.com databases. A total of 16,672 study types (e.g., Case reports or Clinical trials) published in the year 2021 and with at least one altmetric mention were retrieved. The altmetric indicators considered were Altmetric Attention Score (AAS), News mentions, Twitter mentions, and Mendeley readers. Once the dataset had been created, the first step was to carry out a descriptive study. Then a normality hypothesis was contrasted by means of the Kolmogorov-Smirnov test, and since it was significant in all cases, the overall comparison of groups was performed using the non-parametric Kruskal-Wallis test. When this test rejected the null hypothesis, pair-by-pair comparisons were performed with the Mann-Whitney U test, and the intensity of the possible association was measured using Cramers V coefficient. The results suggest that the data do not fit a normal distribution. The Mann-Whitney U test revealed coincidences in five groups of study types, the altmetric indicator with most coincidences being news mentions and the study types with the most coincidences were the systematic reviews together with the meta-analyses, which coincided with four altmetric indicators. Likewise, between the study types and the altmetric indicators, a weak but significant association was observed through the chi-square and Cramers V. It is concluded that the positive association between altmetrics and study types in medicine could reflect the level of the pyramid of scientific evidence.
翻译:本研究的主要目的是分析COVID-19医学研究的Altmetric提及是否与研究类型及其证据水平相关联。数据来自PubMed和Altmetric.com数据库。共检索到2021年发表的16,672种类型的研究(例如病例报告或临床试验),并至少有一次Altmetric提及。考虑的Altmetric指标包括Altmetric Attention Score(AAS)、新闻提及、Twitter提及和Mendeley读者。创建数据集后,第一步是进行描述性研究。然后通过Kolmogorov-Smirnov检验对正态性假设进行验证,由于在所有情况下都是显著的,因此使用非参数Kruskal-Wallis检验进行群体的总体比较。当该检验拒绝了零假设时,使用Mann-Whitney U检验进行一对一对比,并使用Cramers V系数测量可能关联的强度。结果表明,数据不符合正态分布。Mann-Whitney U检验揭示了五组研究类型的巧合,其中新闻提及是最多的Altmetric指标,最多的巧合研究类型与系统综述以及Meta分析相同,它们与四个Altmetric指标相吻合。同样,在研究类型和Altmetric指标之间观察到了弱但有意义的关联,通过卡方和Cramers V测量。结论是,医学领域中Altmetrics与研究类型之间的正向关联可以反映科学证据金字塔的水平。