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Developing Stress Management Programs in a Public Primary Healthcare Institution: Should We Consider Health Workers’ Sociodemographic Groups?

Background and Objectives: An essential part of occupational stress management is identifying target groups and developing a wellbeing program that tailors interventions to the specific needs of the target groups. This study aims to explore whether psychosocial risk determinants and organizational i...

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Autores principales: Dudutienė, Daiva, Juodaitė-Račkauskienė, Audronė, Stukas, Rimantas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7231065/
https://www.ncbi.nlm.nih.gov/pubmed/32260214
http://dx.doi.org/10.3390/medicina56040162
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author Dudutienė, Daiva
Juodaitė-Račkauskienė, Audronė
Stukas, Rimantas
author_facet Dudutienė, Daiva
Juodaitė-Račkauskienė, Audronė
Stukas, Rimantas
author_sort Dudutienė, Daiva
collection PubMed
description Background and Objectives: An essential part of occupational stress management is identifying target groups and developing a wellbeing program that tailors interventions to the specific needs of the target groups. This study aims to explore whether psychosocial risk determinants and organizational intervention objects differ across employees’ groups based on sociodemographic factors in a Lithuanian public primary healthcare institution. Methods: All 690 health workers of the institution were invited to participate (response rate 68%) in a cross-sectional survey between February and March 2017. The questionnaire contained items related to sociodemographic factors (gender, age, job seniority, education, and occupation), 14 psychosocial risk determinants, and 10 organisational intervention objects. Results: The results of the study showed that differences by gender were not statistically significant except for one organisational intervention object (work–life balance). Only a few organisational intervention objects (justice of reward, matching to the job demand, and variety of tasks) had mean rank scores differing statistically across age and job seniority groups. Five organisational intervention objects (work–life balance, variety of tasks, communication, manager feedback, and stress management training) had mean rank scores differing statistically across education groups, and all organisational intervention objects (except stress management training) had mean rank scores differing statistically across occupational groups. Regarding psychosocial risk determinants, excessive work pace had mean rank scores differing statistically across age and job seniority groups. Four (overtime, unclear role, conflicting roles, and being under-skilled) and six psychosocial risk determinants (work overload, overtime, tight deadlines, unclear role, being under-skilled, and responsibility) had mean scores differing statistically across education and occupational groups, respectively. Statistical significance was considered with p-value < 0.05 and 95% confidence interval. Conclusions: The findings showed that different psychosocial risk determinants and organizational interventional objects were emphasized by different sociodemographic groups in the institution, but they did not impact groups in the same measure. Therefore, it is crucial to start by determining the risk group’s specific needs before developing and implementing stress management programs.
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spelling pubmed-72310652020-05-22 Developing Stress Management Programs in a Public Primary Healthcare Institution: Should We Consider Health Workers’ Sociodemographic Groups? Dudutienė, Daiva Juodaitė-Račkauskienė, Audronė Stukas, Rimantas Medicina (Kaunas) Article Background and Objectives: An essential part of occupational stress management is identifying target groups and developing a wellbeing program that tailors interventions to the specific needs of the target groups. This study aims to explore whether psychosocial risk determinants and organizational intervention objects differ across employees’ groups based on sociodemographic factors in a Lithuanian public primary healthcare institution. Methods: All 690 health workers of the institution were invited to participate (response rate 68%) in a cross-sectional survey between February and March 2017. The questionnaire contained items related to sociodemographic factors (gender, age, job seniority, education, and occupation), 14 psychosocial risk determinants, and 10 organisational intervention objects. Results: The results of the study showed that differences by gender were not statistically significant except for one organisational intervention object (work–life balance). Only a few organisational intervention objects (justice of reward, matching to the job demand, and variety of tasks) had mean rank scores differing statistically across age and job seniority groups. Five organisational intervention objects (work–life balance, variety of tasks, communication, manager feedback, and stress management training) had mean rank scores differing statistically across education groups, and all organisational intervention objects (except stress management training) had mean rank scores differing statistically across occupational groups. Regarding psychosocial risk determinants, excessive work pace had mean rank scores differing statistically across age and job seniority groups. Four (overtime, unclear role, conflicting roles, and being under-skilled) and six psychosocial risk determinants (work overload, overtime, tight deadlines, unclear role, being under-skilled, and responsibility) had mean scores differing statistically across education and occupational groups, respectively. Statistical significance was considered with p-value < 0.05 and 95% confidence interval. Conclusions: The findings showed that different psychosocial risk determinants and organizational interventional objects were emphasized by different sociodemographic groups in the institution, but they did not impact groups in the same measure. Therefore, it is crucial to start by determining the risk group’s specific needs before developing and implementing stress management programs. MDPI 2020-04-03 /pmc/articles/PMC7231065/ /pubmed/32260214 http://dx.doi.org/10.3390/medicina56040162 Text en © 2020 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/).
spellingShingle Article
Dudutienė, Daiva
Juodaitė-Račkauskienė, Audronė
Stukas, Rimantas
Developing Stress Management Programs in a Public Primary Healthcare Institution: Should We Consider Health Workers’ Sociodemographic Groups?
title Developing Stress Management Programs in a Public Primary Healthcare Institution: Should We Consider Health Workers’ Sociodemographic Groups?
title_full Developing Stress Management Programs in a Public Primary Healthcare Institution: Should We Consider Health Workers’ Sociodemographic Groups?
title_fullStr Developing Stress Management Programs in a Public Primary Healthcare Institution: Should We Consider Health Workers’ Sociodemographic Groups?
title_full_unstemmed Developing Stress Management Programs in a Public Primary Healthcare Institution: Should We Consider Health Workers’ Sociodemographic Groups?
title_short Developing Stress Management Programs in a Public Primary Healthcare Institution: Should We Consider Health Workers’ Sociodemographic Groups?
title_sort developing stress management programs in a public primary healthcare institution: should we consider health workers’ sociodemographic groups?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7231065/
https://www.ncbi.nlm.nih.gov/pubmed/32260214
http://dx.doi.org/10.3390/medicina56040162
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