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      Health Investment Management and Healthcare Quality in the Public System: A Gender Perspective

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          Abstract

          The aim of this empirical research was to provide useful information for health system managers on the costs and investments involved in improving the quality of the National Health Service (NHS) based on patient assessments and from a gender perspective, i.e., without assuming that the perceived experience is identical for men and women. A cross-sectional study of 31 variables was applied using partial least squares structural equation modeling (PLS-SEM) as a research tool. The data were obtained from the Spanish Ministry of Health, Consumption, and Social Welfare for the entire Spanish territory between 2005 and 2018. The influence of expenditure, resource allocation, and mortality was hypothesized with regard to patient satisfaction according to disconfirmation theory. Patient satisfaction reflects clinical effectiveness, and therefore is a measure of health system quality. The results show that women are more sensitive to public investment in health than men, i.e., an increase in the level of spending and resources increases satisfaction more in women. In both sexes, the level of expenditure has a direct influence on patient satisfaction, and therefore on the quality of the healthcare system. It is important to increase spending on primary care, especially on specialized medical care and diagnostic equipment. However, reducing the use of drugs in favor of alternative treatments or therapies is considered to be positive. Likewise, spending has an impact on available resources, and these, in turn, have a positive influence on the level of use and a negative impact on mortality. Resources, especially healthcare staff, nuclear magnetic resonance equipment, and the number of posts in day hospitals, increase patients’ positive perception of the NHS.

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          Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

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            Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

            G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.
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              PLS-SEM: Indeed a Silver Bullet

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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                26 February 2021
                March 2021
                : 18
                : 5
                : 2304
                Affiliations
                Department of Economics and Business, University of Almería, 04120 Almeria, Spain; aramirez@ 123456ual.es (A.R.-O.); mayragrasso21@ 123456gmail.com (M.S.G.)
                Author notes
                [* ]Correspondence: mcvalls@ 123456ual.es ; Tel.: +34-950-01-55-35
                Author information
                https://orcid.org/0000-0002-9250-717X
                https://orcid.org/0000-0001-6716-1560
                Article
                ijerph-18-02304
                10.3390/ijerph18052304
                7967670
                33652724
                016e1440-76e8-4dde-8420-bf955c26ae46
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 19 November 2020
                : 21 February 2021
                Categories
                Article

                Public health
                national health service,healthcare quality,patient satisfaction,health policy,gender perspective,partial least squares structural equation modelling (pls-sem)

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