LIVIVO - Search results -

Search results

Result 1 - 10 of total 94

Search options

Article ; Online: The Joint Commission's New and Revised Workplace Violence Prevention Standards for Hospitals: A Major Step Forward Toward Improved Quality and Safety.

Arnetz, Judith E

Joint Commission journal on quality and patient safety

2022 Volume 48, Issue 4, Page(s) 241–245

MeSH term(s)	Hospitals ; Humans ; Joint Commission on Accreditation of Healthcare Organizations ; United States ; Workplace ; Workplace Violence/prevention & control
Language	English
Publishing date	2022-02-05
Publishing country	Netherlands
Document type	Journal Article ; Research Support, U.S. Gov't, P.H.S.
ZDB-ID	1189890-2
ISSN	1938-131X ; 1549-425X ; 1553-7250 ; 1070-3241 ; 1549-3741
ISSN (online)	1938-131X ; 1549-425X
ISSN	1553-7250 ; 1070-3241 ; 1549-3741
DOI	10.1016/j.jcjq.2022.02.001
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Accessible to users with ZB MED library card

In stock of ZB MED Cologne/Königswinter

Zs.B 2055: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
ab Jg. 2022: Lesesaal (EG)

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Details ▾

Article ; Online: Workplace violence, work-related exhaustion, and workplace cognitive failure among nurses: A cross-sectional study.

Arnetz, Judith E / Baker, Nathan / Arble, Eamonn / Arnetz, Bengt B

Journal of advanced nursing

2024

Abstract: Aim: To examine the relationships between nurses' exposure to workplace violence and self-reports of workplace cognitive failure.: Design: A cross-sectional study.: Methods: An online questionnaire was administered in April 2023 to nurses in ... ...

Abstract	Aim: To examine the relationships between nurses' exposure to workplace violence and self-reports of workplace cognitive failure. Design: A cross-sectional study. Methods: An online questionnaire was administered in April 2023 to nurses in Michigan, US. Structural equation modelling was used to examine effects of physical and non-physical workplace violence (occupational stressors) and work efficiency and competence development (occupational protective factors) on workplace cognitive failure. Results: Physical violence was a significant predictor of the action subscale of cognitive failure. There were no direct effects of non-physical violence, workplace efficiency, or competence development on any of the workplace cognitive failure dimensions. Both types of violence and efficiency had significant indirect effects on workplace cognitive failure via work-related exhaustion. Work-related exhaustion predicted significantly higher scores for workplace cognitive failure. Conclusion: Workplace violence and work efficiency exhibited primarily indirect effects on workplace cognitive failure among nurses via work-related exhaustion. Implications for the profession and/or patient care: Nurses experiencing workplace violence may be at increased risk for workplace cognitive failure, especially if they are also experiencing work-related exhaustion. Workplaces that nurses perceive as more efficient can help to mitigate the effects of violence on nurses' cognitive failure. Impact: This study addressed the possible effects of workplace violence as well as work efficiency and competence development on nurses' cognitive failure at work. Analyses revealed primarily indirect effects of workplace violence, and indirect protective effects of work efficiency, on nurses' cognitive failure via work-related exhaustion. This research has implications for healthcare organizations and suggests that efforts made by healthcare workplaces to prevent violence and work-related exhaustion, and to enhance work efficiency, may help to mitigate workplace cognitive failure among nurses. Reporting method: We have followed the STROBE checklist in reporting this study. Patient or public contribution: No Patient or public contribution.
Language	English
Publishing date	2024-05-07
Publishing country	England
Document type	Journal Article
ZDB-ID	197634-5
ISSN	1365-2648 ; 0309-2402
ISSN (online)	1365-2648
ISSN	0309-2402
DOI	10.1111/jan.16217
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.B 1851: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article: Designing and Evaluating Opioid Misuse Prevention Training for Rural Communities and Health Care Providers.

Eschbach, Cheryl L / Arnetz, Bengt B / Arnetz, Judith E

Health promotion practice

2023 , Page(s) 15248399231174920

Abstract: Through Substance Abuse and Mental Health Services Administration funding, Michigan State University (MSU) Extension partnered with MSU's Family Medicine and Health Department of Northwest Michigan to implement trainings for community members and health ... ...

Abstract	Through Substance Abuse and Mental Health Services Administration funding, Michigan State University (MSU) Extension partnered with MSU's Family Medicine and Health Department of Northwest Michigan to implement trainings for community members and health care providers to increase awareness and improve prevention efforts addressing opioid use disorder (OUD) in rural areas. We formed the Michigan Substance Use Prevention, Education and Recovery (MiSUPER) project to design and evaluate opioid misuse prevention trainings. A socio-ecological prevention model was an underlying conceptual framework for this project and drove strategies used in trainings, products created, and measurement. The purpose of this study is to determine the effectiveness of one-time online educational training events for rural community members and health care providers on community OUD issues, treatment options, and supports for those in recovery. Between 2020 and 2022, rural participants completed pre- and posttraining, and 30-day follow-up evaluation surveys. We report the demographic characteristics of community (
Language	English
Publishing date	2023-05-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	2036801-X
ISSN	1552-6372 ; 1524-8399
ISSN (online)	1552-6372
ISSN	1524-8399
DOI	10.1177/15248399231174920
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Increased Substance Use among Nurses during the COVID-19 Pandemic.

Arble, Eamonn / Manning, Dana / Arnetz, Bengt B / Arnetz, Judith E

International journal of environmental research and public health

2023 Volume 20, Issue 3

Abstract: There is growing evidence that the COVID-19 pandemic has had a severe impact on the nursing profession worldwide. Occupational strain has disrupted nurses' emotional wellbeing and may have led to negative coping behaviors, such as increased substance use, ...

Abstract	There is growing evidence that the COVID-19 pandemic has had a severe impact on the nursing profession worldwide. Occupational strain has disrupted nurses' emotional wellbeing and may have led to negative coping behaviors, such as increased substance use, which could impair cognitive functioning. The aim of this study was to examine whether increased substance use in a sample of U.S. nurses during the pandemic was related to greater workplace cognitive failure. An online questionnaire was administered in May 2020 to Michigan nurses statewide via three nursing organizations (n = 695 respondents). A path model was used to test the direct effects of reported increased substance use on workplace cognitive failure and via parallel psychological mediators. The model had excellent fit to the observed data, with statistically significant, unique mediating effects of greater symptoms of anxiety (b = 0.236, z = 2.22,
MeSH term(s)	Humans ; COVID-19/epidemiology ; Pandemics ; Emotions ; Adaptation, Psychological ; Substance-Related Disorders/epidemiology ; Nurses
Language	English
Publishing date	2023-02-02
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph20032674
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Accessible to users with ZB MED library card

Order via subito

Details ▾
- Full text online
- Order with fees

Article ; Online: Applying machine learning to explore the association between biological stress and near misses in emergency medicine residents.

Xu, Sonnet / Arnetz, Judith E / Arnetz, Bengt B

PloS one

2022 Volume 17, Issue 3, Page(s) e0264957

Abstract: Physician stress is associated with near misses and adverse medical events. However, little is known about physiological mechanisms linking stress to such events. We explored the utility of machine learning to determine whether the catabolic stress ... ...

Abstract	Physician stress is associated with near misses and adverse medical events. However, little is known about physiological mechanisms linking stress to such events. We explored the utility of machine learning to determine whether the catabolic stress hormone cortisol and the anabolic, anti-stress hormone dehydroepiandrosterone sulfate (DHEA-S), as well as the cortisol to DHEA-S ratio relate to near misses in emergency medicine residents during active duty in a trauma 1 emergency department. Compared to statistical models better suited for inference, machine learning models allow for prediction in situations that have not yet occurred, and thus better suited for clinical applications. This exploratory study used multiple machine learning models to determine possible relationships between biomarkers and near misses. Of the various models tested, support vector machine with radial bias function kernels and support vector machine with linear kernels performed the best, with training accuracies of 85% and 79% respectively. When evaluated on a test dataset, both models had prediction accuracies of around 80%. The pre-shift cortisol to DHEA-S ratio was shown to be the most important predictor in interpretable models tested. Results suggest that interventions that help emergency room physicians relax before they begin their shift could reduce risk of errors and improve patient and physician outcomes. This pilot demonstrates promising results regarding using machine learning to better understand the stress biology of near misses. Future studies should use larger groups and relate these variables to information in electronic medical records, such as objective and patient-reported quality measures.
MeSH term(s)	Dehydroepiandrosterone/metabolism ; Emergency Medicine ; Humans ; Hydrocortisone/metabolism ; Machine Learning ; Near Miss, Healthcare ; Stress, Physiological
Chemical Substances	Dehydroepiandrosterone (459AG36T1B) ; Hydrocortisone (WI4X0X7BPJ)
Language	English
Publishing date	2022-03-08
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0264957
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Development and validation of the provider assessed quality of consultations with language interpretation scale (PQC-LI).

Müller, Frank / Ngo, Julie / Arnetz, Judith E / Holman, Harland T

BMC research notes

2024 Volume 17, Issue 1, Page(s) 15

Abstract: Objective: With the growing immigrant communities in the western world, there is an urgent need to address language barriers to care, and health disparities as a whole. Studies on limited English proficiency patients (LEP) have focused on patient ... ...

Abstract	Objective: With the growing immigrant communities in the western world, there is an urgent need to address language barriers to care, and health disparities as a whole. Studies on limited English proficiency patients (LEP) have focused on patient perspectives of office visits, however little is known about health care provider perspectives of medical visits using interpretive services. We aimed to develop a pragmatic brief questionnaire for assessing providers' views of the quality of communication in outpatient visits with patients with LEP. The questionnaire was validated in a cross-sectional study (n = 99) using principal component analyses (PCA) with oblimin rotation. Internal consistency was analyzed using Cronbach's alpha. Results: Based on theory and literature, a seven-item scale was developed that captures two relevant concepts: (1) Provider - patient interaction during the consultation and (2) perceived quality of translation. The questionnaire was used to assess 99 LEP consultations and demonstrated good feasibility in a clinical setting. PCA revealed the two theory-based components with good factor loadings and internal consistency of α = 0.77. These preliminary results indicate that the questionnaire provides medical professionals with a validated tool to evaluate LEP patient encounters. Further confirmatory validation of the Provider-assessed Quality of Consultations with Language Interpretation (PQC-LI) in larger samples is warranted.
MeSH term(s)	Humans ; Cross-Sectional Studies ; Language ; Communication ; Communication Barriers ; Surveys and Questionnaires
Language	English
Publishing date	2024-01-04
Publishing country	England
Document type	Journal Article
ZDB-ID	2413336-X
ISSN	1756-0500 ; 1756-0500
ISSN (online)	1756-0500
ISSN	1756-0500
DOI	10.1186/s13104-023-06675-7
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Increased Substance Use among Nurses during the COVID-19 Pandemic

Eamonn Arble / Dana Manning / Bengt B. Arnetz / Judith E. Arnetz

International Journal of Environmental Research and Public Health, Vol 20, Iss 2674, p

2023 Volume 2674

Abstract: There is growing evidence that the COVID-19 pandemic has had a severe impact on the nursing profession worldwide. Occupational strain has disrupted nurses’ emotional wellbeing and may have led to negative coping behaviors, such as increased substance use, ...

Abstract	There is growing evidence that the COVID-19 pandemic has had a severe impact on the nursing profession worldwide. Occupational strain has disrupted nurses’ emotional wellbeing and may have led to negative coping behaviors, such as increased substance use, which could impair cognitive functioning. The aim of this study was to examine whether increased substance use in a sample of U.S. nurses during the pandemic was related to greater workplace cognitive failure. An online questionnaire was administered in May 2020 to Michigan nurses statewide via three nursing organizations (n = 695 respondents). A path model was used to test the direct effects of reported increased substance use on workplace cognitive failure and via parallel psychological mediators. The model had excellent fit to the observed data, with statistically significant, unique mediating effects of greater symptoms of anxiety (b = 0.236, z = 2.22, p = 0.027), posttraumatic stress disorder (b = 0.507, z = 4.62, p < 0.001) and secondary trauma (b = 1.10, z = 2.82, p = 0.005). Importantly, the direct effect of increased substance use on workplace cognitive failure was not statistically significant independent of the mediators (b = 0.133, z = 0.56, p = 0.576; 95% confidence interval: −0.33, 0.60). These results point to the importance of further delineating the mechanistic pathways linking adverse stress to workplace cognitive failure. As we emerge from the pandemic, healthcare systems should focus resources on supporting cognitive health by addressing the psychological and emotional welfare of nurses, many of whom may be struggling with residual trauma and increased substance use.
Keywords	nurses ; cognitive failure ; COVID-19 ; trauma ; Medicine ; R
Subject code	150
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Development and validation of the provider assessed quality of consultations with language interpretation scale (PQC-LI)

Frank Müller / Julie Ngo / Judith E. Arnetz / Harland T. Holman

BMC Research Notes, Vol 17, Iss 1, Pp 1-

2024 Volume 6

Abstract: Abstract Objective With the growing immigrant communities in the western world, there is an urgent need to address language barriers to care, and health disparities as a whole. Studies on limited English proficiency patients (LEP) have focused on patient ...

Abstract	Abstract Objective With the growing immigrant communities in the western world, there is an urgent need to address language barriers to care, and health disparities as a whole. Studies on limited English proficiency patients (LEP) have focused on patient perspectives of office visits, however little is known about health care provider perspectives of medical visits using interpretive services. We aimed to develop a pragmatic brief questionnaire for assessing providers’ views of the quality of communication in outpatient visits with patients with LEP. The questionnaire was validated in a cross-sectional study (n = 99) using principal component analyses (PCA) with oblimin rotation. Internal consistency was analyzed using Cronbach’s alpha. Results Based on theory and literature, a seven-item scale was developed that captures two relevant concepts: (1) Provider - patient interaction during the consultation and (2) perceived quality of translation. The questionnaire was used to assess 99 LEP consultations and demonstrated good feasibility in a clinical setting. PCA revealed the two theory-based components with good factor loadings and internal consistency of α = 0.77. These preliminary results indicate that the questionnaire provides medical professionals with a validated tool to evaluate LEP patient encounters. Further confirmatory validation of the Provider-assessed Quality of Consultations with Language Interpretation (PQC-LI) in larger samples is warranted.
Keywords	Language barrier ; Migrant ; Limited English language proficiency ; Questionnaire ; Patient-centeredness ; Interpreters ; Medicine ; R ; Biology (General) ; QH301-705.5 ; Science (General) ; Q1-390
Subject code	420
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Applying machine learning to explore the association between biological stress and near misses in emergency medicine residents.

Sonnet Xu / Judith E Arnetz / Bengt B Arnetz

PLoS ONE, Vol 17, Iss 3, p e

2022 Volume 0264957

Abstract	Physician stress is associated with near misses and adverse medical events. However, little is known about physiological mechanisms linking stress to such events. We explored the utility of machine learning to determine whether the catabolic stress hormone cortisol and the anabolic, anti-stress hormone dehydroepiandrosterone sulfate (DHEA-S), as well as the cortisol to DHEA-S ratio relate to near misses in emergency medicine residents during active duty in a trauma 1 emergency department. Compared to statistical models better suited for inference, machine learning models allow for prediction in situations that have not yet occurred, and thus better suited for clinical applications. This exploratory study used multiple machine learning models to determine possible relationships between biomarkers and near misses. Of the various models tested, support vector machine with radial bias function kernels and support vector machine with linear kernels performed the best, with training accuracies of 85% and 79% respectively. When evaluated on a test dataset, both models had prediction accuracies of around 80%. The pre-shift cortisol to DHEA-S ratio was shown to be the most important predictor in interpretable models tested. Results suggest that interventions that help emergency room physicians relax before they begin their shift could reduce risk of errors and improve patient and physician outcomes. This pilot demonstrates promising results regarding using machine learning to better understand the stress biology of near misses. Future studies should use larger groups and relate these variables to information in electronic medical records, such as objective and patient-reported quality measures.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Applying machine learning to explore the association between biological stress and near misses in emergency medicine residents

Sonnet Xu / Judith E. Arnetz / Bengt B. Arnetz

PLoS ONE, Vol 17, Iss

2022 Volume 3

Abstract	Physician stress is associated with near misses and adverse medical events. However, little is known about physiological mechanisms linking stress to such events. We explored the utility of machine learning to determine whether the catabolic stress hormone cortisol and the anabolic, anti-stress hormone dehydroepiandrosterone sulfate (DHEA-S), as well as the cortisol to DHEA-S ratio relate to near misses in emergency medicine residents during active duty in a trauma 1 emergency department. Compared to statistical models better suited for inference, machine learning models allow for prediction in situations that have not yet occurred, and thus better suited for clinical applications. This exploratory study used multiple machine learning models to determine possible relationships between biomarkers and near misses. Of the various models tested, support vector machine with radial bias function kernels and support vector machine with linear kernels performed the best, with training accuracies of 85% and 79% respectively. When evaluated on a test dataset, both models had prediction accuracies of around 80%. The pre-shift cortisol to DHEA-S ratio was shown to be the most important predictor in interpretable models tested. Results suggest that interventions that help emergency room physicians relax before they begin their shift could reduce risk of errors and improve patient and physician outcomes. This pilot demonstrates promising results regarding using machine learning to better understand the stress biology of near misses. Future studies should use larger groups and relate these variables to information in electronic medical records, such as objective and patient-reported quality measures.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top

Full text online

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED