公众对基于风险的癌症早期检测创新技术的接受度与采用意愿数据表1

数据集获取（Figshare）

• 源站标题：Data Sheet 1_The public are receptive to risk-based innovations: a multi-methods exploration of anticipated acceptability and uptake of novel technologies for cancer early detection in symptomatic and asymptomatic scenarios.pdf
• DOI：10.3389/fcacs.2025.1522609.s001
• 发布日期：2025-02-12
• 文件数：1 个
• 文件总体积：1.05 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_The_public_are_receptive_to_risk-based_innovations_a_multi-methods_exploration_of_anticipated_acceptability_and_uptake_of_novel_technologies_for_cancer_early_detection_in_symptomatic_and_asymptomatic_scenarios_pdf/28397672

命令行下载

curl -L -o "Data Sheet 1_The public are receptive to risk-based innovations: a multi-methods exploration of anticipated acceptability and uptake of novel technolo.pdf" "https://ndownloader.figshare.com/files/52295177"

文件清单

• Data Sheet 1_The public are receptive to risk-based innovations: a multi-methods exploration of anticipated acceptability and uptake of novel technolo.pdf（1.05 MB）

文件总体积 1.05 MB（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

IntroductionNew technologies and innovations are emerging that enable stratification of individuals based on their risk of cancer and enable screening or diagnostic investigations to be targeted to those at greatest need. This study aimed to explore, in depth, attitudes of the UK public toward this concept; specifically, anticipated acceptability and uptake, including barriers and enablers toward uptake.
MethodsA survey was completed independently by a representative population sample and alongside a researcher in think aloud interviews. Participants considered three of six exemplars of innovations that enable risk assessment: polygenic risk scores, geodemographic segmentation, continuous biomarker monitoring, minimally invasive tests, artificial intelligence analysis of medical records, and wearable devices. Questions about likelihood of taking up the risk assessment, acceptability of risk-stratified healthcare, and comfort about risk results being used within healthcare generally were set in asymptomatic then symptomatic scenarios. Descriptive statistics and multivariable logistic regression were used to explore differences between the exemplars and contexts and the impact of individual characteristics. Interviews were analyzed using codebook thematic analysis guided by the Theoretical Framework of Acceptability. Free-text comments were also analyzed thematically.
Results999 participants completed the survey independently and 21 participants completed interviews. Most were extremely or somewhat likely to take up risk assessments, ranging from 62.0% for geodemographic segmentation to 85.2% for minimally invasive tests in the asymptomatic scenario, and from 64.2% for geodemographic segmentation to 94.0% for minimally invasive tests in the symptomatic scenario. Acceptability of using the exemplars within risk-stratified screening or referral pathways followed a similar pattern, as did comfort with the results being used widely. Qualitative analyses showed that the innovations and risk-based approach were viewed as proactive and logical. Tests requiring low burden were preferred, although most participants did not consider the burden of any of the innovations to b

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcacs.2025.1522609.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_The_public_are_receptive_to_risk-based_innovations_a_multi-methods_exploration_of_anticipated_acceptability_and_uptake_of_novel_technologies_for_cancer_early_detection_in_symptomatic_and_asymptomatic_scenarios_pdf/28397672）。