Determinants of primary care workforce variation in England.


Journal

The British journal of general practice : the journal of the Royal College of General Practitioners
ISSN: 1478-5242
Titre abrégé: Br J Gen Pract
Pays: England
ID NLM: 9005323

Informations de publication

Date de publication:
Jun 2020
Historique:
entrez: 20 6 2020
pubmed: 20 6 2020
medline: 20 6 2020
Statut: ppublish

Résumé

The General Practice Forward View (GPFV) outlined how the government plans to attain a strengthened model of general practice. A key component of this proposal is an expansion of the workforce by employing a varied range of practitioners, in other words 'skill mix'. A significant proportion of this investment focuses on increasing the number of 'new' roles such as clinical pharmacists, physiotherapists, physician associates, and paramedics. The aim of this study is to examine what practice characteristics are associated with the current employment of these 'new' roles. The study uses practice level workforce data (2015-2019), publicly available from NHS Digital. The authors model FTE of specific workforce groups (for example, advanced nurse) as a function of deprivation, practice rurality, patient demographics (total list size and percentage of patients aged >65 years) and FTEs from other staff groups. Although analysis is ongoing, initial estimation suggests that the employment of 'new' roles has occurred in larger practices (in terms of list size), in practices with a higher proportion of patients living in deprived areas and practices with a larger proportion of patients aged >65 years. FTE for advanced nurses is negatively associated with GP FTE. A negative correlation between advanced nurse FTE and GP FTE is potentially suggestive of substitution between roles, deliberate or otherwise. For example, practices may employ 'new' roles if they are unable to recruit GPs or they may recruit staff to free up GP time. Further work is needed to confirm these findings and to explore the reasons behind practice employment decisions.

Sections du résumé

BACKGROUND BACKGROUND
The General Practice Forward View (GPFV) outlined how the government plans to attain a strengthened model of general practice. A key component of this proposal is an expansion of the workforce by employing a varied range of practitioners, in other words 'skill mix'. A significant proportion of this investment focuses on increasing the number of 'new' roles such as clinical pharmacists, physiotherapists, physician associates, and paramedics.
AIM OBJECTIVE
The aim of this study is to examine what practice characteristics are associated with the current employment of these 'new' roles.
METHOD METHODS
The study uses practice level workforce data (2015-2019), publicly available from NHS Digital. The authors model FTE of specific workforce groups (for example, advanced nurse) as a function of deprivation, practice rurality, patient demographics (total list size and percentage of patients aged >65 years) and FTEs from other staff groups.
RESULTS RESULTS
Although analysis is ongoing, initial estimation suggests that the employment of 'new' roles has occurred in larger practices (in terms of list size), in practices with a higher proportion of patients living in deprived areas and practices with a larger proportion of patients aged >65 years. FTE for advanced nurses is negatively associated with GP FTE.
CONCLUSION CONCLUSIONS
A negative correlation between advanced nurse FTE and GP FTE is potentially suggestive of substitution between roles, deliberate or otherwise. For example, practices may employ 'new' roles if they are unable to recruit GPs or they may recruit staff to free up GP time. Further work is needed to confirm these findings and to explore the reasons behind practice employment decisions.

Identifiants

pubmed: 32554664
pii: 70/suppl_1/bjgp20X711389
doi: 10.3399/bjgp20X711389
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© British Journal of General Practice 2020.

Auteurs

Jon Gibson (J)

University of Manchester.

Sharon Spooner (S)

University of Manchester.

Matt Sutton (M)

University of Manchester.

Classifications MeSH