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Negative emotions experienced by healthcare staff following medication administration errors: a descriptive study using text-mining and content analysis of incident data

BACKGROUND: Medication errors regardless of the degree of patient harm can have a negative emotional impact on the healthcare staff involved. The potential for self-victimization of healthcare staff following medication errors can add to the moral distress of healthcare staff. The stigma associated...

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Autores principales: Mahat, Sanu, Rafferty, Anne Marie, Vehviläinen-Julkunen, Katri, Härkänen, Marja
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9719256/
https://www.ncbi.nlm.nih.gov/pubmed/36463187
http://dx.doi.org/10.1186/s12913-022-08818-1
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author Mahat, Sanu
Rafferty, Anne Marie
Vehviläinen-Julkunen, Katri
Härkänen, Marja
author_facet Mahat, Sanu
Rafferty, Anne Marie
Vehviläinen-Julkunen, Katri
Härkänen, Marja
author_sort Mahat, Sanu
collection PubMed
description BACKGROUND: Medication errors regardless of the degree of patient harm can have a negative emotional impact on the healthcare staff involved. The potential for self-victimization of healthcare staff following medication errors can add to the moral distress of healthcare staff. The stigma associated with errors and their disclosure often haunts healthcare professionals, leading them to question their own professional competence. This paper investigates the negative emotions expressed by healthcare staff in their reported medication administration error incidents along with the immediate responses they received from their seniors and colleagues after the incident. METHOD: This is a retrospective study using a qualitative descriptive design and text mining. This study includes free-text descriptions of medication administration error incidents (n = 72,390) reported to National Reporting & Learning System in 2016 from England and Wales. Text-mining by SAS text miner and content analysis was used to analyse the data. RESULTS: Analysis of data led to the extraction of 93 initial codes and two categories i.e., 1) negative emotions expressed by healthcare staff which included 4 sub-categories of feelings: (i) fear; (ii) disturbed; (iii) sadness; (iv) guilt and 2) Immediate response from seniors and colleagues which included 2 sub-categories: (i) Reassurance and support and (ii) Guidance on what to do after an error. CONCLUSION: Negative emotions expressed by healthcare staff when reporting medication errors could be a catalyst for learning and system change. However, negative emotions when internalized as fear, guilt, or self-blame, could have a negative impact on the mental health of individuals concerned, reporting culture, and opportunities for learning from the error. Findings from this study, hence, call for future research to investigate the impact of negative emotions on healthcare staff well-being and identify ways to mitigate these in practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-022-08818-1.
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spelling pubmed-97192562022-12-04 Negative emotions experienced by healthcare staff following medication administration errors: a descriptive study using text-mining and content analysis of incident data Mahat, Sanu Rafferty, Anne Marie Vehviläinen-Julkunen, Katri Härkänen, Marja BMC Health Serv Res Research BACKGROUND: Medication errors regardless of the degree of patient harm can have a negative emotional impact on the healthcare staff involved. The potential for self-victimization of healthcare staff following medication errors can add to the moral distress of healthcare staff. The stigma associated with errors and their disclosure often haunts healthcare professionals, leading them to question their own professional competence. This paper investigates the negative emotions expressed by healthcare staff in their reported medication administration error incidents along with the immediate responses they received from their seniors and colleagues after the incident. METHOD: This is a retrospective study using a qualitative descriptive design and text mining. This study includes free-text descriptions of medication administration error incidents (n = 72,390) reported to National Reporting & Learning System in 2016 from England and Wales. Text-mining by SAS text miner and content analysis was used to analyse the data. RESULTS: Analysis of data led to the extraction of 93 initial codes and two categories i.e., 1) negative emotions expressed by healthcare staff which included 4 sub-categories of feelings: (i) fear; (ii) disturbed; (iii) sadness; (iv) guilt and 2) Immediate response from seniors and colleagues which included 2 sub-categories: (i) Reassurance and support and (ii) Guidance on what to do after an error. CONCLUSION: Negative emotions expressed by healthcare staff when reporting medication errors could be a catalyst for learning and system change. However, negative emotions when internalized as fear, guilt, or self-blame, could have a negative impact on the mental health of individuals concerned, reporting culture, and opportunities for learning from the error. Findings from this study, hence, call for future research to investigate the impact of negative emotions on healthcare staff well-being and identify ways to mitigate these in practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-022-08818-1. BioMed Central 2022-12-03 /pmc/articles/PMC9719256/ /pubmed/36463187 http://dx.doi.org/10.1186/s12913-022-08818-1 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
Mahat, Sanu
Rafferty, Anne Marie
Vehviläinen-Julkunen, Katri
Härkänen, Marja
Negative emotions experienced by healthcare staff following medication administration errors: a descriptive study using text-mining and content analysis of incident data
title Negative emotions experienced by healthcare staff following medication administration errors: a descriptive study using text-mining and content analysis of incident data
title_full Negative emotions experienced by healthcare staff following medication administration errors: a descriptive study using text-mining and content analysis of incident data
title_fullStr Negative emotions experienced by healthcare staff following medication administration errors: a descriptive study using text-mining and content analysis of incident data
title_full_unstemmed Negative emotions experienced by healthcare staff following medication administration errors: a descriptive study using text-mining and content analysis of incident data
title_short Negative emotions experienced by healthcare staff following medication administration errors: a descriptive study using text-mining and content analysis of incident data
title_sort negative emotions experienced by healthcare staff following medication administration errors: a descriptive study using text-mining and content analysis of incident data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9719256/
https://www.ncbi.nlm.nih.gov/pubmed/36463187
http://dx.doi.org/10.1186/s12913-022-08818-1
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