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A Bayesian network model to predict the role of hospital noise, annoyance, and sensitivity in quality of patient care

BACKGROUND: Hospital noise can adversely impact nurses’ health, their cognitive function and emotion and in turn, influence the quality of patient care and patient safety. Thus, the aim of this study was to predict the contributing roles of exposure to hospital noise, staff noise-sensitivity and ann...

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Autores principales: Abbasi, Milad, Yazdanirad, Saied, Zokaei, Mojtaba, Falahati, Mohsen, Eyvazzadeh, Nazila
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9435418/
https://www.ncbi.nlm.nih.gov/pubmed/36050728
http://dx.doi.org/10.1186/s12912-022-00948-5
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author Abbasi, Milad
Yazdanirad, Saied
Zokaei, Mojtaba
Falahati, Mohsen
Eyvazzadeh, Nazila
author_facet Abbasi, Milad
Yazdanirad, Saied
Zokaei, Mojtaba
Falahati, Mohsen
Eyvazzadeh, Nazila
author_sort Abbasi, Milad
collection PubMed
description BACKGROUND: Hospital noise can adversely impact nurses’ health, their cognitive function and emotion and in turn, influence the quality of patient care and patient safety. Thus, the aim of this study was to predict the contributing roles of exposure to hospital noise, staff noise-sensitivity and annoyance, on the quality of patient care. METHODS: This descriptive and cross-sectional study was carried out among nurses in an Iranian hospital. To determine nurses’ noise exposure level, the noise was measured in 1510 locations across the hospital in accordance with ISO 9612 standards using KIMO DB 300/2 sound level meter and analyzer. An online survey was used to collect nurses’ individual data. Study questionnaires included demographics, Weinstein noise sensitivity scale, noise annoyance scale, and quality of patient care scale. Finally, to analyze the data, Bayesian Networks (BNs), as probabilistic and graphical models, were used. RESULTS: For the high noise exposure state, high noise sensitivity, and high annoyance, with the probability of 100%, the probability of delivering a desirable quality of patient care decreased by 21, 14, and 23%, respectively. Moreover, at the concurrently high noise exposure and high noise sensitivity with the probability of 100%, the desirable quality of patient care decreased by 26%. The Bayesian most influence value was related to the association of noise exposure and annoyance (0.636). Moreover, annoyance had the highest association with the physical aspect of quality of care (0.400) and sensitivity had the greatest association with the communication aspect (0.283). CONCLUSION: Annoyance induced from environmental noise and personal sensitivity affected the quality of patient care adversely. Moreover, noise and sensitivity had a separate direct adverse effect upon the quality of patient care, and their co-occurrence reduced the potential for delivering quality patient care.
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spelling pubmed-94354182022-09-01 A Bayesian network model to predict the role of hospital noise, annoyance, and sensitivity in quality of patient care Abbasi, Milad Yazdanirad, Saied Zokaei, Mojtaba Falahati, Mohsen Eyvazzadeh, Nazila BMC Nurs Research BACKGROUND: Hospital noise can adversely impact nurses’ health, their cognitive function and emotion and in turn, influence the quality of patient care and patient safety. Thus, the aim of this study was to predict the contributing roles of exposure to hospital noise, staff noise-sensitivity and annoyance, on the quality of patient care. METHODS: This descriptive and cross-sectional study was carried out among nurses in an Iranian hospital. To determine nurses’ noise exposure level, the noise was measured in 1510 locations across the hospital in accordance with ISO 9612 standards using KIMO DB 300/2 sound level meter and analyzer. An online survey was used to collect nurses’ individual data. Study questionnaires included demographics, Weinstein noise sensitivity scale, noise annoyance scale, and quality of patient care scale. Finally, to analyze the data, Bayesian Networks (BNs), as probabilistic and graphical models, were used. RESULTS: For the high noise exposure state, high noise sensitivity, and high annoyance, with the probability of 100%, the probability of delivering a desirable quality of patient care decreased by 21, 14, and 23%, respectively. Moreover, at the concurrently high noise exposure and high noise sensitivity with the probability of 100%, the desirable quality of patient care decreased by 26%. The Bayesian most influence value was related to the association of noise exposure and annoyance (0.636). Moreover, annoyance had the highest association with the physical aspect of quality of care (0.400) and sensitivity had the greatest association with the communication aspect (0.283). CONCLUSION: Annoyance induced from environmental noise and personal sensitivity affected the quality of patient care adversely. Moreover, noise and sensitivity had a separate direct adverse effect upon the quality of patient care, and their co-occurrence reduced the potential for delivering quality patient care. BioMed Central 2022-09-01 /pmc/articles/PMC9435418/ /pubmed/36050728 http://dx.doi.org/10.1186/s12912-022-00948-5 Text en © The Author(s) 2022 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
Abbasi, Milad
Yazdanirad, Saied
Zokaei, Mojtaba
Falahati, Mohsen
Eyvazzadeh, Nazila
A Bayesian network model to predict the role of hospital noise, annoyance, and sensitivity in quality of patient care
title A Bayesian network model to predict the role of hospital noise, annoyance, and sensitivity in quality of patient care
title_full A Bayesian network model to predict the role of hospital noise, annoyance, and sensitivity in quality of patient care
title_fullStr A Bayesian network model to predict the role of hospital noise, annoyance, and sensitivity in quality of patient care
title_full_unstemmed A Bayesian network model to predict the role of hospital noise, annoyance, and sensitivity in quality of patient care
title_short A Bayesian network model to predict the role of hospital noise, annoyance, and sensitivity in quality of patient care
title_sort bayesian network model to predict the role of hospital noise, annoyance, and sensitivity in quality of patient care
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9435418/
https://www.ncbi.nlm.nih.gov/pubmed/36050728
http://dx.doi.org/10.1186/s12912-022-00948-5
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