five

The influence of culture, structure, and human agency on interprofessional learning in a neurosurgical practice learning setting: a case study|跨专业学习数据集|神经外科数据集

收藏
DataCite Commons2021-05-20 更新2024-07-28 收录
跨专业学习
神经外科
下载链接:
https://tandf.figshare.com/articles/dataset/The_influence_of_culture_structure_and_human_agency_on_interprofessional_learning_in_a_neurosurgical_practice_learning_setting_a_case_study/12850664
下载链接
链接失效反馈
资源简介:
The World Health Organization supports the notion that interprofessional learning (IPL) improves healthcare outcomes and contributes to safe, effective, and high-quality care. Consequently, IPL is an integral component within most UK undergraduate healthcare programs. Although much is written about IPL, research to date has mainly focused on the classroom or simulation lab as a setting for IPL. Less is known about how the practice learning environment influences the experiences and outcomes for those involved. A case study research design, situated within a critical realist framework, was undertaken which aimed to better understand how IPL was facilitated for undergraduate healthcare students within a neurosurgical practice learning setting. Interviews, non-participatory observations, and secondary documentary data were used as the methods of data collection to inform the case. Thematic analysis was undertaken, and the findings clustered into overarching themes of culture, structure, and human agency, facilitating a more in-depth exploration of the complex interplay between the factors influencing IPL in the study setting. IPL was supported within the setting which operated as an ‘interprofessional community of practice,’ facilitating student engagement and investing in its staff for the benefit of the patients who had complex neurological needs. A practice-based IPL Multi-Dimensional Assessment Tool was also created to enable colleagues in practice learning environments worldwide to better understand their capability and capacity for the facilitation of practice-based IPL.
提供机构:
Taylor & Francis
创建时间:
2020-08-24
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
点击留言
数据主题
具身智能
数据集  4098个
机构  8个
大模型
数据集  439个
机构  10个
无人机
数据集  37个
机构  6个
指令微调
数据集  36个
机构  6个
蛋白质结构
数据集  50个
机构  8个
空间智能
数据集  21个
机构  5个
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集

YOLO Drone Detection Dataset

为了促进无人机检测模型的开发和评估,我们引入了一个新颖且全面的数据集,专门为训练和测试无人机检测算法而设计。该数据集来源于Kaggle上的公开数据集,包含在各种环境和摄像机视角下捕获的多样化的带注释图像。数据集包括无人机实例以及其他常见对象,以实现强大的检测和分类。

github 收录

LIDC-IDRI

LIDC-IDRI 数据集包含来自四位经验丰富的胸部放射科医师的病变注释。 LIDC-IDRI 包含来自 1010 名肺部患者的 1018 份低剂量肺部 CT。

OpenDataLab 收录

UAVDT

UAVDT数据集由中国科学院大学等机构创建,包含约80,000帧从10小时无人机拍摄视频中精选的图像,覆盖多种复杂城市环境。数据集主要关注车辆目标,每帧均标注了边界框及多达14种属性,如天气条件、飞行高度、相机视角等。该数据集旨在推动无人机视觉技术在不受限制场景下的研究,解决高密度、小目标、相机运动等挑战,适用于物体检测、单目标跟踪和多目标跟踪等基础视觉任务。

arXiv 收录

LinkedIn Salary Insights Dataset

LinkedIn Salary Insights Dataset 提供了全球范围内的薪资数据,包括不同职位、行业、地理位置和经验水平的薪资信息。该数据集旨在帮助用户了解薪资趋势和市场行情,支持职业规划和薪资谈判。

www.linkedin.com 收录

RFUAV

RFUAV数据集是由浙江科技大学信息科学与工程学院开发的高质量原始射频数据集,包含37种不同无人机的约1.3 TB原始频率数据。该数据集旨在解决现有无人机检测数据集类型单一、数据量不足、信号-to-噪声比(SNR)范围有限等问题,提供了丰富的SNR级别和用于特征提取的基准预处理方法及模型评估工具。数据集适用于射频无人机检测和识别,有助于推动相关技术的研究与应用。

arXiv 收录