Rjm Knevel1, M G Gussy2, J Farmer3. 1. Department of Dentistry and Oral Health, College of Science, Health & Engineering, La Trobe University, Bendigo, Vic., Australia. 2. La Trobe University, Bendigo, Vic., Australia. 3. Centre for Social Impact, Swinburne University, Sydney, NSW, Australia.
Abstract
OBJECTIVES: The purpose of this study was to scope the literature that exists about factors influencing oral health workforce planning and management in developing countries (DCs). METHODS: The Arksey and O'Malley method for conducting a scoping review was used. A replicable search strategy was applied, using three databases. Factors influencing oral health workforce planning and management in DCs identified in the eligible articles were charted. FINDINGS: Four thousand citations were identified; 41 papers were included for review. Most included papers were situational analyses. Factors identified were as follows: lack of data, focus on the restorative rather than preventive care in practitioner education, recent increase in number of dental schools (mostly private) and dentistry students, privatization of dental care services which has little impact on care maldistribution, and debates about skill mix and scope of practice. Oral health workforce management in the eligible studies has a bias towards dentist-led systems. Due to a lack of country-specific oral health related data in developing or least developed countries (LDCs), oral health workforce planning often relies on data and modelling from other countries. DISCUSSION AND CONCLUSION: Approaches to oral health workforce management and planning in developing or LDCs are often characterized by approaches to increase numbers of dentists, thus not ameliorating maldistribution of service accessibility. Governments appear to be reducing support for public and preventative oral healthcare, favouring growth in privatized dental services. Changes to professional education are necessary to trigger a paradigm shift to the preventive approach and to improve relationships between different oral healthcare provider roles. This needs to be premised on greater appreciation of preventive care in health systems and funding models.
OBJECTIVES: The purpose of this study was to scope the literature that exists about factors influencing oral health workforce planning and management in developing countries (DCs). METHODS: The Arksey and O'Malley method for conducting a scoping review was used. A replicable search strategy was applied, using three databases. Factors influencing oral health workforce planning and management in DCs identified in the eligible articles were charted. FINDINGS: Four thousand citations were identified; 41 papers were included for review. Most included papers were situational analyses. Factors identified were as follows: lack of data, focus on the restorative rather than preventive care in practitioner education, recent increase in number of dental schools (mostly private) and dentistry students, privatization of dental care services which has little impact on care maldistribution, and debates about skill mix and scope of practice. Oral health workforce management in the eligible studies has a bias towards dentist-led systems. Due to a lack of country-specific oral health related data in developing or least developed countries (LDCs), oral health workforce planning often relies on data and modelling from other countries. DISCUSSION AND CONCLUSION: Approaches to oral health workforce management and planning in developing or LDCs are often characterized by approaches to increase numbers of dentists, thus not ameliorating maldistribution of service accessibility. Governments appear to be reducing support for public and preventative oral healthcare, favouring growth in privatized dental services. Changes to professional education are necessary to trigger a paradigm shift to the preventive approach and to improve relationships between different oral healthcare provider roles. This needs to be premised on greater appreciation of preventive care in health systems and funding models.