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Quality of life measurement in community-based aged care – understanding variation between clients and between care service providers
BACKGROUND: Measuring person-centred outcomes and using this information to improve service delivery is a challenge for many care providers. We aimed to identify predictors of QoL among older adults receiving community-based aged care services and examine variation across different community care se...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8240205/ https://www.ncbi.nlm.nih.gov/pubmed/34182935 http://dx.doi.org/10.1186/s12877-021-02254-2 |
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author | Siette, Joyce Jorgensen, Mikaela L. Georgiou, Andrew Dodds, Laura McClean, Tom Westbrook, Johanna I. |
author_facet | Siette, Joyce Jorgensen, Mikaela L. Georgiou, Andrew Dodds, Laura McClean, Tom Westbrook, Johanna I. |
author_sort | Siette, Joyce |
collection | PubMed |
description | BACKGROUND: Measuring person-centred outcomes and using this information to improve service delivery is a challenge for many care providers. We aimed to identify predictors of QoL among older adults receiving community-based aged care services and examine variation across different community care service outlets. METHODS: A retrospective sample of 1141 Australians aged ≥60 years receiving community-based care services from a large service provider within 19 service outlets. Clients’ QoL was captured using the ICEpop CAPability Index. QoL scores and predictors of QoL (i.e. sociodemographic, social participation and service use) were extracted from clients’ electronic records and examined using multivariable regression. Funnel plots were used to examine variation in risk-adjusted QoL scores across service outlets. RESULTS: Mean age was 81.5 years (SD = 8) and 75.5% were women. Clients had a mean QoL score of 0.81 (range 0–1, SD = 0.15). After accounting for other factors, being older (p < 0.01), having lower-level care needs (p < 0.01), receiving services which met needs for assistance with activities of daily living (p < 0.01), and having higher levels of social participation (p < 0.001) were associated with higher QoL scores. Of the 19 service outlets, 21% (n = 4) had lower mean risk-adjusted QoL scores than expected (< 95% control limits) and 16% (n = 3) had higher mean scores than expected. CONCLUSION: Using QoL as an indicator to compare care quality may be feasible, with appropriate risk adjustment. Implementing QoL tools allows providers to measure and monitor their performance and service outcomes, as well as identify clients with poor quality of life who may need extra support. TRIAL REGISTRATION: Australian and New Zealand clinical trial registry number: ACTRN12617001212347. Registered 18/08/2017. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-021-02254-2. |
format | Online Article Text |
id | pubmed-8240205 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82402052021-06-29 Quality of life measurement in community-based aged care – understanding variation between clients and between care service providers Siette, Joyce Jorgensen, Mikaela L. Georgiou, Andrew Dodds, Laura McClean, Tom Westbrook, Johanna I. BMC Geriatr Research BACKGROUND: Measuring person-centred outcomes and using this information to improve service delivery is a challenge for many care providers. We aimed to identify predictors of QoL among older adults receiving community-based aged care services and examine variation across different community care service outlets. METHODS: A retrospective sample of 1141 Australians aged ≥60 years receiving community-based care services from a large service provider within 19 service outlets. Clients’ QoL was captured using the ICEpop CAPability Index. QoL scores and predictors of QoL (i.e. sociodemographic, social participation and service use) were extracted from clients’ electronic records and examined using multivariable regression. Funnel plots were used to examine variation in risk-adjusted QoL scores across service outlets. RESULTS: Mean age was 81.5 years (SD = 8) and 75.5% were women. Clients had a mean QoL score of 0.81 (range 0–1, SD = 0.15). After accounting for other factors, being older (p < 0.01), having lower-level care needs (p < 0.01), receiving services which met needs for assistance with activities of daily living (p < 0.01), and having higher levels of social participation (p < 0.001) were associated with higher QoL scores. Of the 19 service outlets, 21% (n = 4) had lower mean risk-adjusted QoL scores than expected (< 95% control limits) and 16% (n = 3) had higher mean scores than expected. CONCLUSION: Using QoL as an indicator to compare care quality may be feasible, with appropriate risk adjustment. Implementing QoL tools allows providers to measure and monitor their performance and service outcomes, as well as identify clients with poor quality of life who may need extra support. TRIAL REGISTRATION: Australian and New Zealand clinical trial registry number: ACTRN12617001212347. Registered 18/08/2017. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-021-02254-2. BioMed Central 2021-06-28 /pmc/articles/PMC8240205/ /pubmed/34182935 http://dx.doi.org/10.1186/s12877-021-02254-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Siette, Joyce Jorgensen, Mikaela L. Georgiou, Andrew Dodds, Laura McClean, Tom Westbrook, Johanna I. Quality of life measurement in community-based aged care – understanding variation between clients and between care service providers |
title | Quality of life measurement in community-based aged care – understanding variation between clients and between care service providers |
title_full | Quality of life measurement in community-based aged care – understanding variation between clients and between care service providers |
title_fullStr | Quality of life measurement in community-based aged care – understanding variation between clients and between care service providers |
title_full_unstemmed | Quality of life measurement in community-based aged care – understanding variation between clients and between care service providers |
title_short | Quality of life measurement in community-based aged care – understanding variation between clients and between care service providers |
title_sort | quality of life measurement in community-based aged care – understanding variation between clients and between care service providers |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8240205/ https://www.ncbi.nlm.nih.gov/pubmed/34182935 http://dx.doi.org/10.1186/s12877-021-02254-2 |
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