TY - BOOK

T1 - A mixed methods study exploring early career doctors’ and medical students’ seasonal influenza vaccination

AU - Edge, Rhiannon Louise

PY - 2017

Y1 - 2017

N2 - Seasonal influenza is an acute, contagious respiratory infection that causes considerable morbidity and mortality each year. The Chief Medical Officer for England recommends that healthcare workers have a seasonal influenza vaccination in an attempt to protect both patients and NHS staff. Despite current recommendations and campaigns targeted at improving vaccination uptake, many healthcare workers do not have a seasonal influenza vaccination. It is clear that more research is necessary to fully understand the vaccination decision of healthcare workers.This mixed methods thesis employed a range of novel methodological approaches to understanding the influences on the seasonal influenza vaccination decision by medical students and junior doctors. Social network analysis is a well-established research approach that looks at individuals in the context of their social connections. I used an outbreak simulation model to investigate to assess whether an individual’s risk of infection could be linked with their position in the social network. Expanding further on this, the auto-logistic regression model was applied to social network data to predict an individual’s likelihood of vaccinating given the behaviour of their peers. Finally, a qualitative approach was used to explore the factors informing vaccination decisions.Findings gathered throughout this programme of work were synthesised together to produce a more detailed evaluation of seasonal influenza vaccination amongst medical students and junior doctors. These have been disseminated widely, particularly to occupational health practitioners and the wider academic community – demonstrating that this public health research has impact in practice.By gaining a better understanding of the social effects on influenza vaccination it will be possible to improve seasonal influenza vaccination uptake by healthcare workers, in turn better protecting patients and staff.

AB - Seasonal influenza is an acute, contagious respiratory infection that causes considerable morbidity and mortality each year. The Chief Medical Officer for England recommends that healthcare workers have a seasonal influenza vaccination in an attempt to protect both patients and NHS staff. Despite current recommendations and campaigns targeted at improving vaccination uptake, many healthcare workers do not have a seasonal influenza vaccination. It is clear that more research is necessary to fully understand the vaccination decision of healthcare workers.This mixed methods thesis employed a range of novel methodological approaches to understanding the influences on the seasonal influenza vaccination decision by medical students and junior doctors. Social network analysis is a well-established research approach that looks at individuals in the context of their social connections. I used an outbreak simulation model to investigate to assess whether an individual’s risk of infection could be linked with their position in the social network. Expanding further on this, the auto-logistic regression model was applied to social network data to predict an individual’s likelihood of vaccinating given the behaviour of their peers. Finally, a qualitative approach was used to explore the factors informing vaccination decisions.Findings gathered throughout this programme of work were synthesised together to produce a more detailed evaluation of seasonal influenza vaccination amongst medical students and junior doctors. These have been disseminated widely, particularly to occupational health practitioners and the wider academic community – demonstrating that this public health research has impact in practice.By gaining a better understanding of the social effects on influenza vaccination it will be possible to improve seasonal influenza vaccination uptake by healthcare workers, in turn better protecting patients and staff.

U2 - 10.17635/lancaster/thesis/88

DO - 10.17635/lancaster/thesis/88

M3 - Doctoral Thesis

PB - Lancaster University

ER -