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Investigation of population-based mental health staffing and efficiency-based mental health productivity using an information-theoretic approach

BACKGROUND: Healthcare systems monitor and improve mental health treatment quality, access, continuity and satisfaction through use of population-based and efficiency-based staffing models, the former focused on staffing ratios and the latter, staff productivity. Preliminary evidence suggests that b...

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Autores principales: Boden, Matt, Smith, Clifford A., Trafton, Jodie A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8366961/
https://www.ncbi.nlm.nih.gov/pubmed/34398908
http://dx.doi.org/10.1371/journal.pone.0256268
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author Boden, Matt
Smith, Clifford A.
Trafton, Jodie A.
author_facet Boden, Matt
Smith, Clifford A.
Trafton, Jodie A.
author_sort Boden, Matt
collection PubMed
description BACKGROUND: Healthcare systems monitor and improve mental health treatment quality, access, continuity and satisfaction through use of population-based and efficiency-based staffing models, the former focused on staffing ratios and the latter, staff productivity. Preliminary evidence suggests that both high staffing ratios and moderate-to-high staff productivity are important for ensuring a full continuum of mental health services to indicated populations. METHODS & FINDINGS: With an information-theoretic approach, we conducted a longitudinal investigation of mental health staffing, productivity and treatment at the largest integrated healthcare system in American, the Veterans Health Administration (VHA). VHA facilities (N = 140) served as the unit of measure, with mental health treatment quality, access, continuity and satisfaction predicted by facility staffing and productivity in longitudinal mixed models. An information-theoretic approach: (a) entails the development of a comprehensive set of plausible models that are fit, ranked and weighted to quantitatively assess the relative support for each, and (b) accounts for model uncertainty while identifying best-fit model(s) that include important and exclude unimportant explanatory variables. In best-fit models, higher staffing was the strongest and most consistent predictor of better treatment quality, access, continuity and satisfaction. Higher staff productivity was often, but not always associated with better treatment quality, access, continuity and satisfaction. Results were further nuanced by differential prediction of treatment by between- and within-facility predictor effects and variable interactions. CONCLUSIONS: A population-based mental health staffing ratio and an efficiency-based productivity value are important longitudinal predictors of mental health treatment quality, access, continuity and satisfaction. Our longitudinal design and use of mixed regression models and an information-theoretic approach addresses multiple limitations of prior studies and strengthen our results. Results are discussed in terms of the provision of mental health treatment by healthcare systems, and analytical modeling of treatment quality, access, continuity and satisfaction.
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spelling pubmed-83669612021-08-17 Investigation of population-based mental health staffing and efficiency-based mental health productivity using an information-theoretic approach Boden, Matt Smith, Clifford A. Trafton, Jodie A. PLoS One Research Article BACKGROUND: Healthcare systems monitor and improve mental health treatment quality, access, continuity and satisfaction through use of population-based and efficiency-based staffing models, the former focused on staffing ratios and the latter, staff productivity. Preliminary evidence suggests that both high staffing ratios and moderate-to-high staff productivity are important for ensuring a full continuum of mental health services to indicated populations. METHODS & FINDINGS: With an information-theoretic approach, we conducted a longitudinal investigation of mental health staffing, productivity and treatment at the largest integrated healthcare system in American, the Veterans Health Administration (VHA). VHA facilities (N = 140) served as the unit of measure, with mental health treatment quality, access, continuity and satisfaction predicted by facility staffing and productivity in longitudinal mixed models. An information-theoretic approach: (a) entails the development of a comprehensive set of plausible models that are fit, ranked and weighted to quantitatively assess the relative support for each, and (b) accounts for model uncertainty while identifying best-fit model(s) that include important and exclude unimportant explanatory variables. In best-fit models, higher staffing was the strongest and most consistent predictor of better treatment quality, access, continuity and satisfaction. Higher staff productivity was often, but not always associated with better treatment quality, access, continuity and satisfaction. Results were further nuanced by differential prediction of treatment by between- and within-facility predictor effects and variable interactions. CONCLUSIONS: A population-based mental health staffing ratio and an efficiency-based productivity value are important longitudinal predictors of mental health treatment quality, access, continuity and satisfaction. Our longitudinal design and use of mixed regression models and an information-theoretic approach addresses multiple limitations of prior studies and strengthen our results. Results are discussed in terms of the provision of mental health treatment by healthcare systems, and analytical modeling of treatment quality, access, continuity and satisfaction. Public Library of Science 2021-08-16 /pmc/articles/PMC8366961/ /pubmed/34398908 http://dx.doi.org/10.1371/journal.pone.0256268 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
spellingShingle Research Article
Boden, Matt
Smith, Clifford A.
Trafton, Jodie A.
Investigation of population-based mental health staffing and efficiency-based mental health productivity using an information-theoretic approach
title Investigation of population-based mental health staffing and efficiency-based mental health productivity using an information-theoretic approach
title_full Investigation of population-based mental health staffing and efficiency-based mental health productivity using an information-theoretic approach
title_fullStr Investigation of population-based mental health staffing and efficiency-based mental health productivity using an information-theoretic approach
title_full_unstemmed Investigation of population-based mental health staffing and efficiency-based mental health productivity using an information-theoretic approach
title_short Investigation of population-based mental health staffing and efficiency-based mental health productivity using an information-theoretic approach
title_sort investigation of population-based mental health staffing and efficiency-based mental health productivity using an information-theoretic approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8366961/
https://www.ncbi.nlm.nih.gov/pubmed/34398908
http://dx.doi.org/10.1371/journal.pone.0256268
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