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Demand–Resource Profiles and Job Satisfaction in the Healthcare Sector: A Person-Centered Examination Using Bayesian Informative Hypothesis Testing

Work characteristics may independently and jointly affect well-being, so that whether job demands deplete or energize employees depends on the resources available in the job. However, contradictory results on their joint effects have emerged so far in the literature. We argue that these inconsistenc...

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Autores principales: Marzocchi, Ivan, Ghezzi, Valerio, Di Tecco, Cristina, Ronchetti, Matteo, Ciampa, Valeria, Olivo, Ilaria, Barbaranelli, Claudio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858661/
https://www.ncbi.nlm.nih.gov/pubmed/36673725
http://dx.doi.org/10.3390/ijerph20020967
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author Marzocchi, Ivan
Ghezzi, Valerio
Di Tecco, Cristina
Ronchetti, Matteo
Ciampa, Valeria
Olivo, Ilaria
Barbaranelli, Claudio
author_facet Marzocchi, Ivan
Ghezzi, Valerio
Di Tecco, Cristina
Ronchetti, Matteo
Ciampa, Valeria
Olivo, Ilaria
Barbaranelli, Claudio
author_sort Marzocchi, Ivan
collection PubMed
description Work characteristics may independently and jointly affect well-being, so that whether job demands deplete or energize employees depends on the resources available in the job. However, contradictory results on their joint effects have emerged so far in the literature. We argue that these inconsistencies can be partially explained by two arguments in the contemporary literature in the field. First, most studies in the job design domain are based on classic variable-centered methodologies which, although informative, are not well suited to investigate complex patterns of interactions among multiple variables. Second, these studies have mainly focused on generic work characteristics (e.g., workload, control, support), and are lacking in occupational specificity. Thus, to overcome these limitations, in the current research we include generic and occupation-specific work characteristics and adopt a person-centered approach to (a) identify different patterns of interactions of job demands and resources in a sample of healthcare employees, and (b) determine the degree to which these patterns are associated with employee well-being. We involved a sample of 1513 Italian healthcare providers and collected data on key job demands (workload, emotional dissonance, patient demands and physical demands) and resources (control, management support and peers’ support). We focused on job satisfaction as a broad indicator of well-being. Latent profile analysis revealed four profiles of job demands and resources: high strain–isolated, resourceless, resourceful and active job on the ward. The results of Bayesian informative hypothesis testing showed the highest support for the hypothesis stating that healthcare employees belonging to the active job on the ward profile (medium–high demands, high resources) were the most satisfied. Conversely, employees belonging to the high strain–isolated profile (high demands, low resources) and the resourceless profile (medium–low demands, low resources) were the least satisfied. Overall, our study confirms the key role played by job resources in determining well-being in high-risk sectors, demonstrating that job satisfaction can develop both in challenging and less demanding situations. On a practical level, mapping the complexity of the healthcare psychosocial work environment has important implications, allowing for a better assessment process of employee well-being and helping to identify the most effective and fitting interventions.
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spelling pubmed-98586612023-01-21 Demand–Resource Profiles and Job Satisfaction in the Healthcare Sector: A Person-Centered Examination Using Bayesian Informative Hypothesis Testing Marzocchi, Ivan Ghezzi, Valerio Di Tecco, Cristina Ronchetti, Matteo Ciampa, Valeria Olivo, Ilaria Barbaranelli, Claudio Int J Environ Res Public Health Article Work characteristics may independently and jointly affect well-being, so that whether job demands deplete or energize employees depends on the resources available in the job. However, contradictory results on their joint effects have emerged so far in the literature. We argue that these inconsistencies can be partially explained by two arguments in the contemporary literature in the field. First, most studies in the job design domain are based on classic variable-centered methodologies which, although informative, are not well suited to investigate complex patterns of interactions among multiple variables. Second, these studies have mainly focused on generic work characteristics (e.g., workload, control, support), and are lacking in occupational specificity. Thus, to overcome these limitations, in the current research we include generic and occupation-specific work characteristics and adopt a person-centered approach to (a) identify different patterns of interactions of job demands and resources in a sample of healthcare employees, and (b) determine the degree to which these patterns are associated with employee well-being. We involved a sample of 1513 Italian healthcare providers and collected data on key job demands (workload, emotional dissonance, patient demands and physical demands) and resources (control, management support and peers’ support). We focused on job satisfaction as a broad indicator of well-being. Latent profile analysis revealed four profiles of job demands and resources: high strain–isolated, resourceless, resourceful and active job on the ward. The results of Bayesian informative hypothesis testing showed the highest support for the hypothesis stating that healthcare employees belonging to the active job on the ward profile (medium–high demands, high resources) were the most satisfied. Conversely, employees belonging to the high strain–isolated profile (high demands, low resources) and the resourceless profile (medium–low demands, low resources) were the least satisfied. Overall, our study confirms the key role played by job resources in determining well-being in high-risk sectors, demonstrating that job satisfaction can develop both in challenging and less demanding situations. On a practical level, mapping the complexity of the healthcare psychosocial work environment has important implications, allowing for a better assessment process of employee well-being and helping to identify the most effective and fitting interventions. MDPI 2023-01-05 /pmc/articles/PMC9858661/ /pubmed/36673725 http://dx.doi.org/10.3390/ijerph20020967 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Marzocchi, Ivan
Ghezzi, Valerio
Di Tecco, Cristina
Ronchetti, Matteo
Ciampa, Valeria
Olivo, Ilaria
Barbaranelli, Claudio
Demand–Resource Profiles and Job Satisfaction in the Healthcare Sector: A Person-Centered Examination Using Bayesian Informative Hypothesis Testing
title Demand–Resource Profiles and Job Satisfaction in the Healthcare Sector: A Person-Centered Examination Using Bayesian Informative Hypothesis Testing
title_full Demand–Resource Profiles and Job Satisfaction in the Healthcare Sector: A Person-Centered Examination Using Bayesian Informative Hypothesis Testing
title_fullStr Demand–Resource Profiles and Job Satisfaction in the Healthcare Sector: A Person-Centered Examination Using Bayesian Informative Hypothesis Testing
title_full_unstemmed Demand–Resource Profiles and Job Satisfaction in the Healthcare Sector: A Person-Centered Examination Using Bayesian Informative Hypothesis Testing
title_short Demand–Resource Profiles and Job Satisfaction in the Healthcare Sector: A Person-Centered Examination Using Bayesian Informative Hypothesis Testing
title_sort demand–resource profiles and job satisfaction in the healthcare sector: a person-centered examination using bayesian informative hypothesis testing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858661/
https://www.ncbi.nlm.nih.gov/pubmed/36673725
http://dx.doi.org/10.3390/ijerph20020967
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