Suppr超能文献

cchsflow:一种开放科学方法,用于转换和组合人群健康调查。

cchsflow: an open science approach to transform and combine population health surveys.

机构信息

Ottawa Hospital Research Institute, Civic Campus, ASB 2-012, 1053 Carling Avenue, Ottawa, ON, K1Y 4E9, Canada.

Department of Family Medicine, University of Ottawa, Ottawa, Ontario, Canada.

出版信息

Can J Public Health. 2021 Aug;112(4):714-721. doi: 10.17269/s41997-020-00470-8. Epub 2021 Mar 24.

Abstract

SETTING

The Canadian Community Health Survey (CCHS) is one of the world's largest ongoing cross-sectional population health surveys, with over 130,000 respondents every two years or over 1.1 million respondents since its inception in 2001. While the survey remains relatively consistent over the years, there are differences between cycles that pose a challenge to analyze the survey over time.

INTERVENTION

A program package called cchsflow was developed to transform and harmonize CCHS variables to consistent formats across multiple survey cycles. An open science approach was used to maintain transparency, reproducibility and collaboration.

OUTCOMES

The cchsflow R package uses CCHS survey data between 2001 and 2014. Worksheets were created that identify variables, their names in previous cycles, their category structure, and their final variable names. These worksheets were then used to recode variables in each CCHS cycle into consistently named and labelled variables. Following, survey cycles can be combined. The package was then added as a GitHub repository to encourage collaboration with other researchers.

IMPLICATION

The cchsflow package has been added to the Comprehensive R Archive Network (CRAN) and contains support for over 160 CCHS variables, generating a combined data set of over 1 million respondents. By implementing open science practices, cchsflow aims to minimize the amount of time needed to clean and prepare data for the many CCHS users across Canada.

摘要

背景

加拿大社区健康调查(CCHS)是世界上最大的正在进行的横断面人群健康调查之一,每两年有超过 130,000 名受访者,自 2001 年成立以来已有超过 110 万名受访者。虽然该调查多年来保持相对稳定,但各周期之间存在差异,这给长期分析该调查带来了挑战。

干预措施

开发了一个名为 cchsflow 的程序包,用于将 CCHS 变量转换并协调为多个调查周期中的一致格式。采用开放科学方法来保持透明度、可重复性和协作性。

结果

cchsflow R 包使用了 2001 年至 2014 年期间的 CCHS 调查数据。创建了工作表,用于标识变量、它们在以前周期中的名称、它们的类别结构以及它们的最终变量名称。然后,使用这些工作表将每个 CCHS 周期中的变量重新编码为具有一致命名和标签的变量。接下来,可以组合调查周期。然后,将该程序包作为 GitHub 存储库添加,以鼓励与其他研究人员合作。

影响

cchsflow 包已添加到 Comprehensive R Archive Network(CRAN)中,支持超过 160 个 CCHS 变量,生成了超过 100 万名受访者的综合数据集。通过实施开放科学实践,cchsflow 旨在最大限度地减少加拿大众多 CCHS 用户清理和准备数据所需的时间。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9198/8225753/0a9776bb7134/41997_2020_470_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验