Reader in Maternity Care
Maternity care health services research, Qualitative, narrative and mixed methods research, Informed decision making and personalised care, Risk theory and risk communication, Evaluation of Complex Interventions, Qualitative evidence syntheses, Implementation Science, Knowledge Mobilisation and transfer
Dr. Kirstie Coxon is a Reader in Maternity Care in the ReaCH (Research in Childbirth and Health) group, within the THRIVE Research Centre at University of Central Lancashire. She is a midwife and nurse and has a background in applied health services research and social science research, specialising in qualitative narrative methods, qualitative evidence synthesis, informed or shared decision making, risk theory and risk communication, mixed methods evaluations of complex interventions, process evaluation, knowledge mobilisation and implementation science.
Kirstie has undertaken research in a range of maternity care topics; her focal interest is in women's informed decision making, initially through her PhD on 'Birth Place Decisions'. An NIHR post-doctoral Knowledge Mobilisation fellows
more...Dr. Kirstie Coxon is a Reader in Maternity Care in the ReaCH (Research in Childbirth and Health) group, within the THRIVE Research Centre at University of Central Lancashire. She is a midwife and nurse and has a background in applied health services research and social science research, specialising in qualitative narrative methods, qualitative evidence synthesis, informed or shared decision making, risk theory and risk communication, mixed methods evaluations of complex interventions, process evaluation, knowledge mobilisation and implementation science.
Kirstie has undertaken research in a range of maternity care topics; her focal interest is in women's informed decision making, initially through her PhD on 'Birth Place Decisions'. An NIHR post-doctoral Knowledge Mobilisation fellowship meant Kirstie could spend time developing decision-aids, infographics and evidence briefings, using a participatory approach to involve women, birth partners, midwives, obstetricians, antenatal teachers in developing resources and learning about how differently risks and benefits of births in different settings can be perceived. During this project, Kirstie worked with organisations representing women, NHS trusts and commissioners and with statisticians and designers to generate some of the first 'icon arrays' used in maternity care, and continues to work closely with service user groups and representatives.
Recent research include a process evaluation for the DESiGN trial (Detection of the Small for Gestational Age Neonate) and a qualitative study within the POPPIE pilot RCT of continuity of midwifery care for women at risk of preterm birth; both were hybrid type 2 studies.
Kirstie is an associate editor for 'Midwifery' journal and has also been guest editor for special issues of 'Health, Risk and Society' and 'Midwifery', bringing together a range of research into understandings of risk in maternity care and application of this knowledge to maternity care practice. She is currently working on an updated edited volume on risk in maternity care using cross-national perspectives.
Having been fortunate enough to receive research career funding and training support through personal NIHR fellowships, Kirstie is really keen to support others in developing their research careers. She has worked in research capacity development as an NIHR advocate for research careers in midwifery, and for Nursing and Allied Health Professionals within NIHR ARC S London, providing mentorship to aspiring clinical academics. She is also a current supervisor for masters and doctoral students, and experienced in undergraduate and postgraduate teaching.
PhD in Health Studies Research (2012) King's College London
MA in Health Studies (2005) University of Kent
BSc (Hons) Midwifery (1999) Canterbury Christ Church University
Diploma (H Ed) in Nursing Studies (1995) Canterbury Christ Church University
Registered Midwife
Registered Nurse
NMC registered teacher