Abstract
Aim:
To identify discrete approaches to specialist healthcare support for older care home residents in the UK and to estimate their prevalence.
Background:
Internationally, a range of new initiatives are emerging to meet the multiple and complex healthcare needs of care home residents. However, little is known about their relative effectiveness and, given their heterogeneity, a classification scheme is required to enable research staff to explore this.
Method:
A UK survey collected information on the funding, age, coverage, aims, staffing and activities of 64 specialist care home support services. Latent class analysis (LCA) was used to allocate the sample into subgroups with similar characteristics.
Findings:
Three classes were identified. Class 1 (55% of sample) contained services with a high probability of providing scheduled input (regular preplanned visits) and support for all residents and a moderate probability of undertaking medication management, but a low probability of training care home staff (‘predominantly direct care’). Class 2 (23% of sample) had a moderate/high probability of providing scheduled input, support for all residents, medication management and training (‘direct and indirect care’). Class 3 (22% of sample) had a low probability of providing scheduled input, support for all residents and medication management, but a high probability of providing training for care home staff (‘predominantly indirect care’). Consultants were more likely to be members of services in Class 1 than Class 2, and Class 2 than Class 3.
Conclusions:
LCA offers a promising approach to the creation of a taxonomy of specialist care home support services. The skills and knowledge required by healthcare staff vary between classes, raising important issues for service design. The proposed classification can be used to explore the extent to which different organisational forms are associated with better resident, process and service outcomes.
To identify discrete approaches to specialist healthcare support for older care home residents in the UK and to estimate their prevalence.
Background:
Internationally, a range of new initiatives are emerging to meet the multiple and complex healthcare needs of care home residents. However, little is known about their relative effectiveness and, given their heterogeneity, a classification scheme is required to enable research staff to explore this.
Method:
A UK survey collected information on the funding, age, coverage, aims, staffing and activities of 64 specialist care home support services. Latent class analysis (LCA) was used to allocate the sample into subgroups with similar characteristics.
Findings:
Three classes were identified. Class 1 (55% of sample) contained services with a high probability of providing scheduled input (regular preplanned visits) and support for all residents and a moderate probability of undertaking medication management, but a low probability of training care home staff (‘predominantly direct care’). Class 2 (23% of sample) had a moderate/high probability of providing scheduled input, support for all residents, medication management and training (‘direct and indirect care’). Class 3 (22% of sample) had a low probability of providing scheduled input, support for all residents and medication management, but a high probability of providing training for care home staff (‘predominantly indirect care’). Consultants were more likely to be members of services in Class 1 than Class 2, and Class 2 than Class 3.
Conclusions:
LCA offers a promising approach to the creation of a taxonomy of specialist care home support services. The skills and knowledge required by healthcare staff vary between classes, raising important issues for service design. The proposed classification can be used to explore the extent to which different organisational forms are associated with better resident, process and service outcomes.
Original language | English |
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Journal | Primary Health Care Research & Development |
Volume | 20 |
Early online date | 16 Sept 2019 |
DOIs | |
Publication status | Published - 2019 |