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Workforce requirements in rheumatology: a systematic literature review informing the development of a workforce prediction risk of bias tool and the EULAR points to consider

OBJECTIVE: To summarise the available information on physician workforce modelling, to develop a rheumatology workforce prediction risk of bias tool and to apply it to existing studies in rheumatology. METHODS: A systematic literature review (SLR) was performed in key electronic databases (1946–2017...

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Autores principales: Unger, Julia, Putrik, Polina, Buttgereit, Frank, Aletaha, Daniel, Bianchi, Gerolamo, Bijlsma, Johannes W J, Boonen, Annelies, Cikes, Nada, Dias, João Madruga, Falzon, Louise, Finckh, Axel, Gossec, Laure, Kvien, Tore K, Matteson, Eric L, Sivera, Francisca, Stamm, Tanja A, Szekanecz, Zoltan, Wiek, Dieter, Zink, Angela, Dejaco, Christian, Ramiro, Sofia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6336097/
https://www.ncbi.nlm.nih.gov/pubmed/30714580
http://dx.doi.org/10.1136/rmdopen-2018-000756
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author Unger, Julia
Putrik, Polina
Buttgereit, Frank
Aletaha, Daniel
Bianchi, Gerolamo
Bijlsma, Johannes W J
Boonen, Annelies
Cikes, Nada
Dias, João Madruga
Falzon, Louise
Finckh, Axel
Gossec, Laure
Kvien, Tore K
Matteson, Eric L
Sivera, Francisca
Stamm, Tanja A
Szekanecz, Zoltan
Wiek, Dieter
Zink, Angela
Dejaco, Christian
Ramiro, Sofia
author_facet Unger, Julia
Putrik, Polina
Buttgereit, Frank
Aletaha, Daniel
Bianchi, Gerolamo
Bijlsma, Johannes W J
Boonen, Annelies
Cikes, Nada
Dias, João Madruga
Falzon, Louise
Finckh, Axel
Gossec, Laure
Kvien, Tore K
Matteson, Eric L
Sivera, Francisca
Stamm, Tanja A
Szekanecz, Zoltan
Wiek, Dieter
Zink, Angela
Dejaco, Christian
Ramiro, Sofia
author_sort Unger, Julia
collection PubMed
description OBJECTIVE: To summarise the available information on physician workforce modelling, to develop a rheumatology workforce prediction risk of bias tool and to apply it to existing studies in rheumatology. METHODS: A systematic literature review (SLR) was performed in key electronic databases (1946–2017) comprising an update of an SLR in rheumatology and a hierarchical SLR in other medical fields. Data on the type of workforce prediction models and the factors considered in the models were extracted. Key general as well as specific need/demand and supply factors for workforce calculation in rheumatology were identified. The workforce prediction risk of bias tool was developed and applied to existing workforce studies in rheumatology. RESULTS: In total, 14 studies in rheumatology and 10 studies in other medical fields were included. Studies used a variety of prediction models based on a heterogeneous set of need/demand and/or supply factors. Only two studies attempted empirical validation of the prediction quality of the model. Based on evidence and consensus, the newly developed risk of bias tool includes 21 factors (general, need/demand and supply). The majority of studies revealed high or moderate risk of bias for most of the factors. CONCLUSIONS: The existing evidence on workforce prediction in rheumatology is scarce, heterogeneous and at moderate or high risk of bias. The new risk of bias tool should enable future evaluation of workforce prediction studies. This review informs the European League Against Rheumatism points to consider for the conduction of workforce requirement studies in rheumatology.
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spelling pubmed-63360972019-02-01 Workforce requirements in rheumatology: a systematic literature review informing the development of a workforce prediction risk of bias tool and the EULAR points to consider Unger, Julia Putrik, Polina Buttgereit, Frank Aletaha, Daniel Bianchi, Gerolamo Bijlsma, Johannes W J Boonen, Annelies Cikes, Nada Dias, João Madruga Falzon, Louise Finckh, Axel Gossec, Laure Kvien, Tore K Matteson, Eric L Sivera, Francisca Stamm, Tanja A Szekanecz, Zoltan Wiek, Dieter Zink, Angela Dejaco, Christian Ramiro, Sofia RMD Open Epidemiology OBJECTIVE: To summarise the available information on physician workforce modelling, to develop a rheumatology workforce prediction risk of bias tool and to apply it to existing studies in rheumatology. METHODS: A systematic literature review (SLR) was performed in key electronic databases (1946–2017) comprising an update of an SLR in rheumatology and a hierarchical SLR in other medical fields. Data on the type of workforce prediction models and the factors considered in the models were extracted. Key general as well as specific need/demand and supply factors for workforce calculation in rheumatology were identified. The workforce prediction risk of bias tool was developed and applied to existing workforce studies in rheumatology. RESULTS: In total, 14 studies in rheumatology and 10 studies in other medical fields were included. Studies used a variety of prediction models based on a heterogeneous set of need/demand and/or supply factors. Only two studies attempted empirical validation of the prediction quality of the model. Based on evidence and consensus, the newly developed risk of bias tool includes 21 factors (general, need/demand and supply). The majority of studies revealed high or moderate risk of bias for most of the factors. CONCLUSIONS: The existing evidence on workforce prediction in rheumatology is scarce, heterogeneous and at moderate or high risk of bias. The new risk of bias tool should enable future evaluation of workforce prediction studies. This review informs the European League Against Rheumatism points to consider for the conduction of workforce requirement studies in rheumatology. BMJ Publishing Group 2018-12-05 /pmc/articles/PMC6336097/ /pubmed/30714580 http://dx.doi.org/10.1136/rmdopen-2018-000756 Text en © Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0
spellingShingle Epidemiology
Unger, Julia
Putrik, Polina
Buttgereit, Frank
Aletaha, Daniel
Bianchi, Gerolamo
Bijlsma, Johannes W J
Boonen, Annelies
Cikes, Nada
Dias, João Madruga
Falzon, Louise
Finckh, Axel
Gossec, Laure
Kvien, Tore K
Matteson, Eric L
Sivera, Francisca
Stamm, Tanja A
Szekanecz, Zoltan
Wiek, Dieter
Zink, Angela
Dejaco, Christian
Ramiro, Sofia
Workforce requirements in rheumatology: a systematic literature review informing the development of a workforce prediction risk of bias tool and the EULAR points to consider
title Workforce requirements in rheumatology: a systematic literature review informing the development of a workforce prediction risk of bias tool and the EULAR points to consider
title_full Workforce requirements in rheumatology: a systematic literature review informing the development of a workforce prediction risk of bias tool and the EULAR points to consider
title_fullStr Workforce requirements in rheumatology: a systematic literature review informing the development of a workforce prediction risk of bias tool and the EULAR points to consider
title_full_unstemmed Workforce requirements in rheumatology: a systematic literature review informing the development of a workforce prediction risk of bias tool and the EULAR points to consider
title_short Workforce requirements in rheumatology: a systematic literature review informing the development of a workforce prediction risk of bias tool and the EULAR points to consider
title_sort workforce requirements in rheumatology: a systematic literature review informing the development of a workforce prediction risk of bias tool and the eular points to consider
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6336097/
https://www.ncbi.nlm.nih.gov/pubmed/30714580
http://dx.doi.org/10.1136/rmdopen-2018-000756
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