结果测量收集计划与方法
该数据集提供跌倒预防警报研究证据采纳效果的混合方法研究方案,包含数据收集时间表与方法。

基本信息
资源简介
结果测量收集计划与方法
【数据集背景】
本数据集源站标题为《Outcome measure collection schedule and approach.》。源站首次发布:2024-12-13。
【数据内容】
源站原始描述:The research-to-practice gap is a well-known phenomenon. The adoption of evidence into clinical practice needs to consider the complexity of the health care system and a multitude of contextual issues. Research evidence is usually a form of extrinsic motivation for practice change, but works best when it aligns with the intrinsic values of the system and the people in it. Health professionals tend to refer to internal, local policies, information sources and procedures more than external academi…
【数据结构与技术规格】
文件数 1 个,合计 5.63 KB。Table 1.xls:application/vnd.ms-excel,5.63 KB。数据集 DOI:10.1371/journal.pone.0314956.t001。
【主题与分类】
源站学科分类:Medicine、Microbiology、Biotechnology、Biological Sciences not elsewhere classified、Cancer、Science Policy。主题标签:protocol paper describes、health professionals tend、another 18 wards、36 hospital wards、research might impact、health care system、clinical practice needs、clinical practice、practice gap、practice change。
【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/Outcome_measure_collection_schedule_and_approach_/28026224
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Pu, Dai; Mitchell, Debra; Brusco, Natasha; Stephen, Kelly; Hutchinson, Ana; Griffith, Anna; et al. (2024). Outcome measure collection schedule and approach.. PLOS ONE. Dataset
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)
- 源站标题:Outcome measure collection schedule and approach.
- DOI:10.1371/journal.pone.0314956.t001
- 发布日期:2024-12-13
- 文件数:1 个
- 文件总体积:5.63 KB
- 源站页面:https://plos.figshare.com/articles/dataset/Outcome_measure_collection_schedule_and_approach_/28026224
命令行下载
curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/51175481"
文件清单
Table 1.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
The research-to-practice gap is a well-known phenomenon. The adoption of evidence into clinical practice needs to consider the complexity of the health care system and a multitude of contextual issues. Research evidence is usually a form of extrinsic motivation for practice change, but works best when it aligns with the intrinsic values of the system and the people in it. Health professionals tend to refer to internal, local policies, information sources and procedures more than external academic research evidence. This protocol paper describes a mixed-methods study with a quasi-experimental design that seeks to investigate how involvement in research might impact the uptake or implementation of recommendations arising from that research. Research evidence for the effectiveness and cost-effectiveness of mobilisation alarms for falls prevention will be disseminated at 36 hospital wards in Victoria, Australia. Eighteen of these wards will be sites where this research evidence was generated; another 18 wards will not have been involved in evidence generation. The uptake of research evidence will be measured across three time points using quantitative and qualitative data.
Trial registration: This study has been registered with the Australian New Zealand Clinical Trials Registry: ACTRN12621000823875p.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0314956.t001(source: https://plos.figshare.com/articles/dataset/Outcome_measure_collection_schedule_and_approach_/28026224)。
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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