Exploring the needs, concerns and knowledge of women diagnosed with gestational diabetes: A qualitative study

Objective
To explore the concerns, needs and knowledge of women diagnosed with Gestational Diabetes Mellitus (GDM).
Design
A qualitative study of women with GDM or a history of GDM.
Methods
Nineteen women who were both pregnant and recently diagnosed with GDM or post- natal with a recent history of GDM were recruited from outpatient diabetes care clinics. This qualitative study utilised focus groups. Participants were asked a series of open-ended questions to explore 1) current knowledge of GDM; 2) anxiety when diagnosed with GDM, and whether this changed overtime; 3) understanding and managing GDM and 4) the future impact of GDM. The data were analysed using a conventional content analysis approach.
Findings
Women experience a steep learning curve when initially diagnosed and eventually become skilled at managing their disease effectively. The use of insulin is associated with fear and guilt. Diet advice was sometimes complex and not culturally appropriate. Women appear not to be fully aware of the short or long-term consequences of a diagnosis of GDM.
Conclusions
Midwives and other Health Care Professionals need to be cognisant of the impact of a diagnosis of GDM and give individual and culturally appropriate advice (especially with regards to diet). High quality, evidence based information resources need to be made available to this group of women. Future health risks and lifestyle changes need to be discussed at diagnosis to ensure women have the opportunity to improve their health.

Towards a knowledge-based approach for effective decision-making in railway safety

Purpose – This paper aims to contribute towards understanding how safety knowledge can be elicited from railway experts for the purposes of supporting effective decision-making. Design/methodology/approach – A consortium of safety experts from across the British railway industry is formed. Collaborative modelling of the knowledge domain is used as an approach to the elicitation of safety knowledge from experts. From this, a series of knowledge models is derived to inform decision-making. This is achieved by using Bayesian networks as a knowledge modelling scheme, underpinning a Safety Prognosis tool to serve meaningful prognostics information and visualise such information to predict safety violations. Findings – Collaborative modelling of safety-critical knowledge is a valid approach to knowledge elicitation and its sharing across the railway industry. This approach overcomes some of the key limitations of existing approaches to knowledge elicitation. Such models become an effective tool for prediction of safety cases by using railway data. This is demonstrated using passenger–train interaction safety data. Practical implications – This study contributes to practice in two main directions: by documenting an effective approach to knowledge elicitation and knowledge sharing, while also helping the transport industry to understand safety. Social implications – By supporting the railway industry in their efforts to understand safety, this research has the potential to benefit railway passengers, staff and communities in general, which is a priority for the transport sector. Originality/value – This research applies a knowledge elicitation approach to understanding safety based on collaborative modelling, which is a novel approach in the context of transport.