杜克大学和KCMC的定性数据主题
包含杜克大学和KCMC医护人员对TBI预后模型实施可行性的定性访谈主题数据,涉及6个影响因素。

基本信息
资源简介
杜克大学和KCMC的定性数据主题
【数据集背景】
本数据集源站标题为《Qualitative data themes at Duke and KCMC.》。源站首次发布:2023-11-28。
【数据内容】
源站原始描述:Traumatic brain injury (TBI) is the most common cause of death and disability globally. TBI, which disproportionately affects low middle-income countries (LMIC), uses significant amounts of health system resources in costly care and management. Innovative solutions are required to address this high burden of TBI. One possible solution is prognostic models which enhance diagnostic ability of physicians, thereby helping to tailor treatments more effectively. This study aims to evaluate the feasibi…
【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.pgph.0002154.t002。
【主题与分类】
源站学科分类:Medicine、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Cancer、Science Policy。主题标签:one possible solution、health system resources、enhance diagnostic ability、considers overarching characteristics、21 participants interviewed、research design methods、knowledge translation method、design interviews、implementation research、duke participants。
【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/Qualitative_data_themes_at_Duke_and_KCMC_/24653245
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:O’Leary, Paige; Domeracki, Alexis; Raymond, Julius; Kozhumam, Arthi; Macha, Victoria; Sakita, Francis; et al. (2023). Qualitative data themes at Duke and KCMC.. PLOS Global Public Health. Dataset
下载信息
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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。
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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)
- 源站标题:Qualitative data themes at Duke and KCMC.
- DOI:10.1371/journal.pgph.0002154.t002
- 发布日期:2023-11-28
- 文件数:1 个
- 文件总体积:5.63 KB
- 源站页面:https://plos.figshare.com/articles/dataset/Qualitative_data_themes_at_Duke_and_KCMC_/24653245
命令行下载
curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/43324048"
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
Traumatic brain injury (TBI) is the most common cause of death and disability globally. TBI, which disproportionately affects low middle-income countries (LMIC), uses significant amounts of health system resources in costly care and management. Innovative solutions are required to address this high burden of TBI. One possible solution is prognostic models which enhance diagnostic ability of physicians, thereby helping to tailor treatments more effectively. This study aims to evaluate the feasibility of a TBI prognostic model developed in Tanzania for use by Kilimanjaro Christian Medical Center (KCMC) healthcare providers and Duke-affiliated healthcare providers using human centered design methodology. Duke participants were included to gain insight from a different context with more established practices to inform the TBI tool implementation strategy at KCMC. To evaluate the feasibility of integrating the TBI tool into potential workflows, co-design interviews were conducted with emergency physicians and nursing staff at KCMC and Duke. Qualitatively, the TBI tool was assessed using human centered design (HCD) techniques. Our research design methods were created using the Consolidated Framework for Implementation Research which considers overarching characteristics of successful implementation to contribute to theory development and verification of implementation strategies across multiple contexts. Our knowledge translation method was guided using the knowledge-to-action framework. Of the 21 participants interviewed, 12 were associated with Duke Hospital, and 9 from Kilimanjaro Christian Medical Centre. Emerging from the data were 6 themes that impacted the implementation of the TBI tool: access, barriers, facilitators, use of the TBI tool, outer setting, and inner setting. To our knowledge, this is the first study to investigate the pre-implementation of a sub-Saharan Africa (SSA) data- based TBI prediction tool using human centered design methodology. Findings of this study will aid in determining under what conditions a TBI prognostic model intervention will work at KCMC.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pgph.0002154.t002(source: https://plos.figshare.com/articles/dataset/Qualitative_data_themes_at_Duke_and_KCMC_/24653245)。
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。
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