Caponnetto Valeria, Dante Angelo, Masotta Vittorio, La Cerra Carmen, Petrucci Cristina, Alfes Celeste Marie, Lancia Loreto
Department of Health, Life and Environmental Sciences, University of L'Aquila, Rita Levi Montalcini Building, G. Petrini Street - 67100, L'Aquila, Italy.
Neuroscience Section, Department of Applied Clinical Sciences and Biotechnology, University of L'Aquila, Vetoio Street - 67100, L'Aquila, Italy.
Data Brief. 2021 Aug 14;38:107298. doi: 10.1016/j.dib.2021.107298. eCollection 2021 Oct.
Data were extracted from observational studies describing undergraduate nursing students' academic outcomes that were included in a systematic review and meta-analysis conducted in 2019 and updated in 2020 [1]. Data were extracted by two researchers independently through a previously tested electronic spreadsheet; any disagreement about data extraction was discussed with a third author. Extracted data were studies' general information, characteristics (i.e., country, study design, involved centers, number of cohort of students involved, duration (years) and denomination of the program attended, sample (), sociodemographic characteristics of the sample, and methods utilized for data collection), and data related to the research question(s) of the review, i.e., nursing students' academic outcomes occurrence and associated factors. Raw data for each included study are reported, along with meta-analyses that were performed using ProMeta free software utilizing Odds Ratio (OR) and Cohen's as principal effect sizes. The random-effect model was used for all studies, while the level of heterogeneity was explored and quantified through the Cochran's Q-test and , respectively. Substantial or considerable heterogeneity (i.e., ≥ 50%) was explored through a subgroup analysis based on the study design, when feasible [2]. A sensitivity analysis was also performed to detect the possible influence of single studies on meta-analyses results [2]. Publication bias was assessed through funnel plots and the testsf for their asymmetry, i.e., Begg and Mazumdar's rank correlation and Egger's linear regression method [2]. These data provide for an updated state of the art about nursing students' outcomes and associated factors. Therefore, they could ease future literature summaries about the topic, other than allow a comparison of the literature with future research results.
数据取自观察性研究，这些研究描述了本科护理专业学生的学业成果，这些成果包含在2019年进行并于2020年更新的一项系统评价和荟萃分析中[1]。数据由两名研究人员通过一个预先测试过的电子表格独立提取；关于数据提取的任何分歧都与第三位作者进行了讨论。提取的数据包括研究的一般信息、特征（即国家、研究设计、涉及的中心、参与学生队列的数量、持续时间（年）以及所参加课程的名称、样本（）、样本的社会人口学特征以及用于数据收集的方法），以及与该评价的研究问题相关的数据，即护理专业学生学业成果的发生情况及相关因素。报告了每项纳入研究的原始数据，以及使用ProMeta免费软件以优势比（OR）和科恩系数作为主要效应量进行的荟萃分析。所有研究均采用随机效应模型，同时分别通过 Cochran's Q检验和 来探索和量化异质性水平。当可行时，通过基于研究设计的亚组分析来探索实质性或相当大的异质性（即≥50%）[2]。还进行了敏感性分析，以检测单项研究对荟萃分析结果的可能影响[2]。通过漏斗图及其不对称性检验（即Begg和Mazumdar的秩相关检验以及Egger的线性回归方法）来评估发表偏倚[2]。这些数据提供了关于护理专业学生成果及相关因素的最新技术水平。因此，它们不仅可以便于未来对该主题的文献进行总结，还能将文献与未来的研究结果进行比较。
