组织评估中的精选问题

包含针对癌症健康不平等问题的组织评估精选问题,用于支持癌症中心DEIJ工作组评估与改进。

Figshare
2026-02-12 更新
浏览 18
文本reported demographic datareducing cancer disparitieslinguistically appropriate servicesimproving accurate socioimplicit bias traininghelp teams assesshealth care deliveryenhanced national culturallyembedded structural biaseducational curriculum aligneddemographic data collection14 sessions designed
Select questions from organizational assessment.

基本信息

模态
文本
大小
0.000005632 GB
许可
CC BY 4.0
创建/更新时间
2026-02-12
版本
v1

资源简介

组织评估中的精选问题

【一句话概览】

本数据集是 figshare 公开的文本数据集,收录 1 个文件、合计 5.63 KB,主题涵盖 reported demographic data、reducing cancer disparities、linguistically appropriate services、improving accurate socio、implicit bias training,可用于医学自然语言处理(实体识别、信息抽取等),CC BY 4.0 许可开放获取。

【数据集背景】

本数据集源站标题为《Select questions from organizational assessment.》。源站首次发布:2026-02-12。

【数据内容】

源站原始描述:Purpose To address observed cancer health inequities resulting from historically-embedded structural bias in quality, equity, and access to health care delivery, we organized and coordinated Diversity, Equity, Inclusion, and Justice (DEIJ) Task Forces across three cancer centers in the Mid-Atlantic region. Methods We recruited multi-disciplinary, diverse faculty and staff for each task force to optimize capacity to implement sustainable, effective strategies for institutional change. We develope…

【数据结构与技术规格】

文件数 1 个,合计 5.63 KB。Table 2.xls:application/vnd.ms-excel,5.63 KB。数据集 DOI:10.1371/journal.pone.0341662.t002。

【主题与分类】

源站学科分类:Medicine、Biotechnology、Marine Biology、Science Policy、Infectious Diseases。主题标签:reported demographic data、reducing cancer disparities、linguistically appropriate services、improving accurate socio、implicit bias training、help teams assess、health care delivery、enhanced national culturally、embedded structural bias、educational curriculum aligned。

【适用方向】

文本类数据可用于医学自然语言处理,如命名实体识别、信息抽取与文本分类。

【获取与许可】

源站页面:https://plos.figshare.com/articles/dataset/_p_Select_questions_from_organizational_assessment_p_/31326614

使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。

规范引用:AuBuchon, Katarina E.; McDonnell, Anne; Arem, Hannah; Taylor, Teletia; Kanaan, Yasmine M; Gondré-Lewis, Marjorie C.; et al. (2026). Select questions from organizational assessment.. PLOS ONE. Dataset

官方服务

专家保障全链路合规授权与标注交付

陕川双基地 × 超十家三甲医院直连

从合规授权、采集治理、专业标注到数据集交付,我们提供全链路闭环服务,让高质量临床数据即拿即用。

⚡️ 需要原始数据或标注支持?联系我们获取定制方案。

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下载信息

注册下载

需要注册 Kaggle 账号并登录后下载,适合需要跟踪下载记录和使用 API 的用户。

暂未开放

公开下载

无需注册即可直接获取公开样本或文档,适合快速预览和评估数据集质量。

免登录

有偿下载

公开数据集受托下载与技术交付服务。

提供高速下载与技术交付服务(收技术服务费,非数据销售)

暂未开放

当前数据集主要来源为 Kaggle 公开托管,完整影像包建议通过原始链接或 Kaggle API 获取。

使用方式

数据集获取(Figshare)

命令行下载

curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/61850885"

文件清单

  • Table 2.xls(5.63 KB)

文件总体积 5.63 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

Purpose
To address observed cancer health inequities resulting from historically-embedded structural bias in quality, equity, and access to health care delivery, we organized and coordinated Diversity, Equity, Inclusion, and Justice (DEIJ) Task Forces across three cancer centers in the Mid-Atlantic region.
Methods
We recruited multi-disciplinary, diverse faculty and staff for each task force to optimize capacity to implement sustainable, effective strategies for institutional change. We developed an educational curriculum aligned with enhanced National Culturally and Linguistically Appropriate Services (CLAS) Standards and the DC Hospital Health Equity Framework. Over 18 months, task force members were invited to 14 sessions designed to help teams assess and prioritize critical areas for improvement through organizational assessments and then create and implement action plans.
Results
Task force goals focused on reducing cancer disparities by improving patient information accessibility, expanding the reach of implicit bias training for providers, and improving accurate socio-demographic data collection. The task forces made meaningful progress on two of these three goals. Challenges included competing time demands, incomplete and limited data generated by institutions, limited insight into operational protocols and inadequate resources and time to complete work.
Conclusion
Clear, accessible patient-reported demographic data, protected staff time, leadership commitment, and significant institutional resource supports are critical to effectively advance DEIJ work.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0341662.t002(source: https://plos.figshare.com/articles/dataset/_p_Select_questions_from_organizational_assessment_p_/31326614)。

数据缺失?

依托陕西、四川两大基地,我们与超过十家三甲医院建立直接合作关系,覆盖合规授权、采集治理、专业标注、数据交付的全流程,为AI医疗团队提供即拿即用的高质量临床数据。

⚡️ 需要数据支持或标注服务?立即联系我们获取专业方案。

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