正确理解质量数据：癌症患者和亲属对呈现格式的随机比较

数据集获取（Figshare）

• 源站标题：Understanding quality data correctly: a randomized comparison of presentation formats among cancer patients and relatives
• DOI：10.6084/m9.figshare.21746098.v1
• 发布日期：2022-12-17
• 文件数：4 个
• 文件总体积：40.68 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Understanding_quality_data_correctly_a_randomized_comparison_of_presentation_formats_among_cancer_patients_and_relatives/21746098

命令行下载

curl -L -o "ionc_a_2153619_sm9895.rtf" "https://ndownloader.figshare.com/files/38581690"
curl -L -o "ionc_a_2153619_sm9893.jpeg" "https://ndownloader.figshare.com/files/38581693"
curl -L -o "ionc_a_2153619_sm9892.jpeg" "https://ndownloader.figshare.com/files/38581696"
curl -L -o "ionc_a_2153619_sm9891.jpeg" "https://ndownloader.figshare.com/files/38581699"

文件清单

• ionc_a_2153619_sm9895.rtf（737 B）
• ionc_a_2153619_sm9893.jpeg（13.56 KB）
• ionc_a_2153619_sm9892.jpeg（13.53 KB）
• ionc_a_2153619_sm9891.jpeg（12.85 KB）

文件总体积 40.68 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Background: Patient representatives are increasingly engaged in quality in health care, and even though quality data are publicly available, correct interpretation may be challenging. We designed a randomized study with the primary aim to examine the association between preferred data presentation format and the interpretation of quality data among cancer patients and relatives.
Material and methods: Surveys were distributed to the Danish Cancer Society Citizens’ Panel between 31 March and 14 April 2019 and 55% completed the survey (N = 464) including six storyboards that presented authentic quality data in table format, league table and point estimates. The storyboards were randomized to expose participants to the data in the three different formats and in varying presentation order. Logistic regression models were used to calculate Odds Ratios (ORs) and 95% confidence intervals (CIs) for the association between preferred presentation format, health literacy, education and cohabitation status as exposures and interpretation of quality data as outcome.
Results: The majority of participants (97%) had high literacy and 57% had a medium or long higher education. A total of 60% found the questions difficult or very difficult and 33% were not able to correctly interpret at least one format. Correct interpretation was associated with preferred league table (OR = 1.62; 95% CI = 1.04–5.52) and if the data was presented in the preferred format. Medium and long education were associated with correct interpretation of at least one format (OR = 1.93; 95% CI = 1.16–3.21 and OR = 3.89; 95% CI = 1.90–7.95, respectively) while health literacy and cohabitation status were not.
Conclusions: More than one third of the participants were not able to correctly interpret the data and the understanding of quality data improved with longer education and if the data was presented in the preferred format. Decision-makers should carefully consider displaying quality data according to preferred presentation format and to guide interpretation for individuals with short education.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.21746098.v1（source: https://tandf.figshare.com/articles/dataset/Understanding_quality_data_correctly_a_randomized_comparison_of_presentation_formats_among_cancer_patients_and_relatives/21746098）。