ArticleBMJ open2019
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.
Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Exploring the use of social network analysis methods in process improvement within healthcare organizations: a scoping review.BMC health services research · 2024Article
- Network approaches and interventions in healthcare settings: A systematic scoping review.PloS one · 2023Article
- Physician participation in quality improvement work- interest and opportunity: a cross-sectional survey.BMC primary care · 2022Article
- An approach to predicting patient experience through machine learning and social network analysis.Journal of the American Medical Informatics Association : JAMIA · 2020Observational
- Methodology of a Social Network Survey in Primary Care Practices With Medical Home Attributes.The Journal of ambulatory care managementArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectivesTo 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'.
designSequential 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.
settingAn acute medical admissions unit, which receives admissions from the emergency department and primary care, in a National Health Service England teaching hospital.
participantsTrainee 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.
resultsClinical-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.
conclusionInformation 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.