Abstract
Aim:
To describe variation in task shifting from general practitioners (GPs) to practice assistants/nurses in 34 countries, and to explain differences by analysing associations with characteristics of the GPs, their practices and features of the health care systems.
Background:
Redistribution of tasks and responsibilities in primary care are driven by changes in demand for care, such as the growing number of patients with chronic conditions, and workforce developments, including staff shortage. The need to manage an expanding range of services has led to adaptations in the skill mix of primary care teams. However, these developments are hampered by barriers between professional domains, which can be rigid as a result of strict regulation, traditional attitudes and lack of trust.
Methods:
Data were collected between 2011 and 2013 through a cross-sectional survey among approximately 7200 GPs in 34 countries. The dependent variable ‘task shifting’ is measured through a composite score of GPs’ self-reported shifting of tasks. Independent variables at GP and practice level are: innovativeness; part-time working; availability of staff; location and population of the practice. Country-level independent variables are: institutional development of primary care; demand for and supply of care; nurse prescribing as an indicator for professional boundaries; professionalisation of practice assistants/nurses (indicated by professional training, professional associations and journals). Multilevel analysis is used to account for the clustering of GPs in countries.
Findings:
Countries vary in the degree of task shifting by GPs. Regarding GP and practice characteristics, use of electronic health record applications (as an indicator for innovativeness) and age of the GPs are significantly related to task shifting. These variables explain only little variance at the level of GPs. Two country variables are positively related to task shifting: nurse prescribing and professionalisation of primary care nursing. Professionalisation has the strongest relationship, explaining 21% of the country variation.
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