Published in:
Open Access 01-12-2020 | Research article
An exploration of the experiences of GP registrar supervisors in small rural communities: a qualitative study
Published in: BMC Health Services Research | Issue 1/2020
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Background
In Australia registrar training to become a general practitioner (GP) involves three to four years of supervised learning with at least 50% of GP registrars training wholly in rural areas. In particular rural over regional GP placements are important for developing future GPs with broader skills because the rural scope of practice is wider. Having enough GP supervisors in smaller rural communities is essential such training. We aimed to explore what makes rural GPs’ based outside of major regional centres, participate in supervising or not, their experiences of supervising, and impact of their practice context.
Methods
Semi-structured interviews were undertaken with 25 GPs based in rural Tasmania (outside of major regions - Hobart and Launceston), in towns of < 25,000 population, to explore the GPs’ professional backgrounds, their experiences of supervising GP registrars, their practice context and their decisions about supervising GP registrars or not. Thematic analysis was undertaken; key ideas, concepts and experiences were identified and then reviewed and further refined to core themes.
Results
Supervising was perceived to positively impact on quality of clinical care, reduce busy-ness and improve patient access to primary care. It was energising for GPs working in rural contexts. Rural GPs noted business factors impacted the decision to participate in supervision and the experience of participating: including uncertainty and discontinuity of registrar supply (rotational training systems), registrar competence and generating income.
Conclusions
Supervising is strongly positive for rural GPs and related to job satisfaction but increasing supervision capacity in rural areas may depend on better policies to assure continuity of rural registrars as well as policies and systems that enable viable supervision models tailored to the context.