Diffusion of knowledge and behaviours among trainee doctors in an acute medical unit and implications for quality improvement work: a mixed methods social network analysis.

Paul Sullivan, Ghazal Saatchi, Izaba Younis, Mary Louise Harris

BMJ OPEN(2019)

引用 5|浏览4
暂无评分
摘要
Objectives To describe the social networks that diffuse knowledge, attitudes and behaviours relating to different domains of practice within teams of trainee doctors in an acute hospital medical setting. The domains examined were 'clinical-technical', 'patient centredness' and 'organisation of work'. Design Sequential mixed methods: (i) sociocentric survey of trainee consisting of questions about which colleagues are emulated or looked to for advice, with construction of social network maps, followed by (ii) semi-structured interviews regarding peer-to-peer influence, analysed using a grounded theory approach. The study took place over 24 months. Setting An acute medical admissions unit, which receives admissions from the emergency department and primary care, in a National Health Service England teaching hospital. Participants Trainee medical doctors working in five consecutive rotational teams. Surveys were done by 39 trainee doctors; then 15 different participants from a maximal diversity sample were interviewed. Results Clinical-technical behaviours spread in a dense network with rich horizontal peer-to-peer connections. Patient-centred behaviours spread in a sparse network. Approaches to non-patient facing work are seldom copied from colleagues. Highly influential individuals for clinical technical memes were identified; high influencers were not identified for the other domains. Conclusion Information and influence relating to different aspects of practice have different patterns of spread within teams of trainee doctors; highly influential individuals were important only for spread of clinical-technical practice. Influencers have particular characteristics, and this knowledge could guide leaders and teachers.
更多
查看译文
关键词
change management,internal medicine,medical education & training
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要