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Article ; Online: Do hospitals attaining a public recognition for treating nurses fairly deliver better-quality health care? Evidence from cross-sectional analysis of California hospitals.

Shen, Hsiu-Chu / Li, Chien-Ching / Yeh, Shu-Chuan Jennifer

2024

Abstract: Aim: This study explored whether hospitals that allocate greater resources to their nursing staff provide better healthcare services than those that invest less in their nursing personnel.: Design: Cross-sectional logistic and tobit analyses.: ... ...

Abstract	Aim: This study explored whether hospitals that allocate greater resources to their nursing staff provide better healthcare services than those that invest less in their nursing personnel. Design: Cross-sectional logistic and tobit analyses. Methods: We examined a sample of 314 California hospitals in 2017. We obtained a hospital's public recognition for treating nurses fairly between 2015 and 2017 from Nurse.org, the largest online community of nurses. We derived a hospital's healthcare quality in 2018 from the 2019-2020 Best Hospitals rankings released by U.S. News, a well-known media company publishing independent healthcare assessments periodically. Results: Our results showed that a nurse-friendly workplace was a crucial determinant of its overall healthcare quality. Conclusion and implications: Healthcare administrators keen to enhance the quality of healthcare services should consider creating nurse-friendly workplaces. Furthermore, their evaluation of nurses' contributions to overall healthcare quality should not solely depend on the nurse-assessed quality of care, but rather comprise not only broad aspects of patient outcomes in primary care but also patient experiences, care-related factors and expert opinions. Patient or public contribution: Our study helped address the overwhelmed healthcare system, whose long-running shortage of nurses has been exacerbated by the COVID-19 pandemic. Our work suggested that a hospital's investment in a nurse-friendly workplace can enhance its acquisition, retention and devotion of the nursing staff. This, in turn, can have profound impacts on its overall healthcare quality. What already is known: Existing empirical evidence on the relation between nurse-friendly workplace and healthcare quality is limited and inconclusive. What this paper adds: We documented evidence that the quality of healthcare services provided by hospitals varies with their treatment of nursing staff. Implications for practice/policy: Our results provided insights into key policies that have the potential to improve healthcare quality.
Language	English
Publishing date	2024-02-21
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.16123
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Article ; Online: Abusive supervision and employee well-being of nursing staff: Mediating role of occupational stress.

Shih, Fang-Chi / Yeh, Shu-Chuan Jennifer / Hsu, Wan-Ling

Journal of advanced nursing

2022 Volume 79, Issue 2, Page(s) 664–675

Abstract: Aim: The study examined whether occupational stress mediated the relationship between abusive supervision and well-being of nursing staff.: Design: A cross-sectional questionnaire survey was administered.: Methods: Data were collected at three- ... ...

Abstract	Aim: The study examined whether occupational stress mediated the relationship between abusive supervision and well-being of nursing staff. Design: A cross-sectional questionnaire survey was administered. Methods: Data were collected at three-time points between July 2020 and January 2021. A total of 313 valid responses were obtained from nurses working in a general hospital in Taiwan. The data were analysed using descriptive statistics, Pearson's correlation analysis and the bootstrap method. Results: Abusive supervision was positively associated with occupational stress (β = 0.288, SE = 0.069, 95% CI [0.152, 0.423]) and negatively associated with employee well-being, including psychological (β = -0.350, SE = 0.084, 95% CI [-0.515, -0.186]), physical (β = -0.301, SE = 0.080, 95% CI [-0.459, -0.143]) and social well-being (β = -0.422, SE = 0.121, 95% CI [-0.661, -0.183]). Occupational stress was negatively related to employee well-being. A mediation analysis with bootstrapping revealed that occupational stress mediated the relationship between abusive supervision and employee well-being, which included psychological (95% bootstrap CI [-0.183, -0.046]), physical (95% bootstrap CI [-0.212, -0.062]) and social well-being (95% bootstrap CI [-0.178, -0.040]). Conclusion: Abusive supervision influences employee well-being. Occupational stress mediates the relationship between abusive supervision and employee well-being. To improve employee well-being, hospital administrators should develop policies for effectively managing nursing supervisors' abusive behaviour and subordinates' stress management. Impact: Abusive supervision increased the occupational stress of employees and influenced their well-being. Thus, educational courses should be implemented to train supervisors to practice positive leadership and treat employees fairly. Promoting stress management among nursing staff may lead to the prompt reporting of abusive events and improved employee well-being. No patient or public contribution: This study investigated the relationship between the abusive supervision and employee well-being of nursing employees. No patient or public contribution is involved in this study.
MeSH term(s)	Humans ; Cross-Sectional Studies ; Leadership ; Occupational Stress ; Surveys and Questionnaires ; Nursing Staff
Language	English
Publishing date	2022-12-13
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.15538
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Article ; Online: Long-term care insurance purchase decisions of registered nurses: Deep learning versus logistic regression models.

Shi, Hon-Yi / Yeh, Shu-Chuan Jennifer / Chou, Hsueh-Chih / Wang, Wen Chun

Health policy (Amsterdam, Netherlands)

2023 Volume 129, Page(s) 104709

Abstract: Objective: The purpose of this study was to use a deep learning model and a traditional statistical regression model to predict the long-term care insurance decisions of registered nurses.: Methods: We Prospectively surveyed 1,373 registered nurses ... ...

Abstract	Objective: The purpose of this study was to use a deep learning model and a traditional statistical regression model to predict the long-term care insurance decisions of registered nurses. Methods: We Prospectively surveyed 1,373 registered nurses with a minimum of 2 years of full-time working experience at a large medical center in Taiwan: 615 who already owned long-term care insurance (LTCI), 332 who had no intention to purchase LTCI (group 1), and 426 who intended to purchase LTCI (group 2). Results: After inverse probability of treatment weighting (IPTW), no statistically significant differences were identified in the study characteristics of the two groups. All the performance indices for the deep neural network (DNN) model were significantly higher than those of the multiple logistic regression (MLR) model (P<0.001). The strongest predictor of an individual's long-term care insurance decision was their risk propensity score, followed by their caregiving responsibilities, whether they live with older adult relatives, their experiences of catastrophic illness, and their openness to experience. Conclusions: The DNN model is useful for predicting long-term care insurance decisions. Its prediction accuracy can be increased through training with temporal data collected from registered nurses. Future research can explore designs for two-level or multilevel models that explain the contextual effects of the risk factors on long-term care insurance decisions.
MeSH term(s)	Humans ; Aged ; Insurance, Long-Term Care ; Logistic Models ; Deep Learning ; Models, Statistical ; Surveys and Questionnaires ; Long-Term Care
Language	English
Publishing date	2023-01-18
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	605805-x
ISSN	1872-6054 ; 0168-8510
ISSN (online)	1872-6054
ISSN	0168-8510
DOI	10.1016/j.healthpol.2023.104709
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Article ; Online: Protective Behaviors for COVID-19 Were Associated With Fewer Psychological Impacts on Nurses: A Cross-Sectional Study in Taiwan.

Yen, Chia-Chi / Chan, Min-Ho / Lin, Wei-Chun / Yeh, Shu-Chuan Jennifer

Inquiry : a journal of medical care organization, provision and financing

2022 Volume 59, Page(s) 469580221096278

Abstract: Objective: The COVID-19 pandemic has increased psychological distress among common people and has caused health care providers, such as nurses, to experience tremendous stress.: Methods: This prospective cross-sectional study assessed the ... ...

Abstract	Objective: The COVID-19 pandemic has increased psychological distress among common people and has caused health care providers, such as nurses, to experience tremendous stress. Methods: This prospective cross-sectional study assessed the psychological impacts on nurses in a community hospital in Taiwan, including major depressive disorder (MDD), posttraumatic stress (PTS), and pessimism. According to transactional theory, coping strategies and personal factors have psychological impacts. We hypothesized that behavioral responses to COVID-19 (problem-focused coping) are more effective in reducing psychological impacts than emotional responses to COVID-19 (emotion-focused coping). Independent variables were the use of behavioral and emotional coping strategies for COVID-19 and 3 personal factors, namely sleep disturbance, physical component summary (PCS-12), and mental component summary (MCS-12) of the 12-Item Short Form Health Survey (SF-12) obtained from the Medical Outcomes Study. Dependent variables comprised 3 psychological impacts, namely MDD, PTS, and pessimism. Results: We determined that behavioral coping strategies had significant negative effects on PTS and pessimism; however, emotional coping strategies had significantly positive effects on PTS and pessimism. Sleep disturbance was significantly associated with increased MDD and pessimism. PCS-12 had a significant negative effect on PTS, whereas MCS-12 was not significantly associated with any of the 3 psychological impacts. Conclusions: Nurses who adopted protective behavior against COVID-19, such as washing hands, wearing masks, avoiding touching eyes, and mouth, and avoiding personal contact, were associated with less posttraumatic stress and pessimism. Healthcare providers should consider strategies for improving preventive behaviors to help ease their worries and fears concerning COVID-19.
MeSH term(s)	Adaptation, Psychological ; COVID-19 ; Cross-Sectional Studies ; Depressive Disorder, Major ; Humans ; Pandemics ; Prospective Studies ; Surveys and Questionnaires ; Taiwan/epidemiology
Language	English
Publishing date	2022-05-09
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	42153-4
ISSN	1945-7243 ; 0046-9580
ISSN (online)	1945-7243
ISSN	0046-9580
DOI	10.1177/00469580221096278
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Article ; Online: Relationship between using cancer resource center services and patient outcomes.

Yeh, Shu-Chuan Jennifer / Wang, Wen Chun / Yu, Hsien-Chung / Wu, Tzu-Yu / Lo, Ying-Ying / Shi, Hon-Yi / Chou, Hsueh-Chih

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer

2023 Volume 31, Issue 12, Page(s) 706

Abstract: Purpose: Psychological and social support are crucial in treating cancer. Cancer resource centers provide patients with cancer and their families with services that can help them through cancer treatment, ensure that patients receive adequate treatment, ...

Abstract	Purpose: Psychological and social support are crucial in treating cancer. Cancer resource centers provide patients with cancer and their families with services that can help them through cancer treatment, ensure that patients receive adequate treatment, and reduce cancer-related stress. These centers offer various services, including medical guidance, health education, emotional assistance (e.g., consultations for cancer care), and access to resources such as financial aid and post recovery programs. In this study, we comprehensively analyzed how cancer resource centers assist patients with cancer and improve their clinical outcomes. Methods: The study participants comprised patients initially diagnosed with head and neck cancer or esophageal cancer. A total of 2442 patients from a medical center in Taiwan were included in the study. Data were analyzed through logistic regression and Cox proportional hazards regression. Results: The results indicate that unemployment, blue-collar work, and a lower education level were associated with higher utilization of cancer resource center services. The patients who were unemployed or engaged in blue-collar work had higher risks of mortality than did their white-collar counterparts. Patient education programs can significantly improve the survival probability of patients with cancer. On the basis of our evaluation of the utilization and benefits of services provided by cancer resource centers, we offer recommendations for improving the functioning of support systems for patients with cancer and provide suggestions for relevant future research. Conclusions: We conclude that cancer resource centers provide substantial support for patients of low socioeconomic status and improve patients' survival.
MeSH term(s)	Humans ; Head and Neck Neoplasms ; Hospitals ; Social Support ; Taiwan
Language	English
Publishing date	2023-11-17
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1134446-5
ISSN	1433-7339 ; 0941-4355
ISSN (online)	1433-7339
ISSN	0941-4355
DOI	10.1007/s00520-023-08169-5
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Article: Private Long-Term Care Insurance Decision: The Role of Income, Risk Propensity, Personality, and Life Experience.

Yeh, Shu-Chuan Jennifer / Wang, Wen Chun / Chou, Hsueh-Chih / Chen, Shih-Hua Sarah

Healthcare (Basel, Switzerland)

2021 Volume 9, Issue 1

Abstract: The rising aging population contributes to increased caregiver burden and a greater need for long-term care services, thereby posing stronger financial burden. The current study aimed to examine the effect of income, risk-taking propensity, personality ... ...

Abstract	The rising aging population contributes to increased caregiver burden and a greater need for long-term care services, thereby posing stronger financial burden. The current study aimed to examine the effect of income, risk-taking propensity, personality traits, and life experience on the ownership of and intention to own private long-term care insurance (LTCI). Primary data were collected from 1373 registered nurses with a minimum of two years of full-time working experience. Multinomial logistic regression was used to examine the relationships between ownership of LTCI and personal discretionary income, risk propensity, openness to experience, and life experience. Personal discretionary income was a crucial positive indicator in predicting ownership of LTCI. Higher risk-taking propensity was found to be negatively related to both currently own and future intention to own private LTCI. Participants who currently live with elders and who agree to caregiving responsibilities with government-provided cash allowance showed future intention to purchase LTCI. Little evidence was found for an association between life experience and future intention to own LTCI. Income, risk-taking propensity, and personality traits differ in their impact on ownership of and future intention to own LTCI. Our results provide policy makers with a better understanding of the forces driving demand in the private LTCI market, as well as the accompanying implications for public LTCI.
Language	English
Publishing date	2021-01-19
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2721009-1
ISSN	2227-9032
ISSN	2227-9032
DOI	10.3390/healthcare9010102
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Article: Emotional Labor in Health Care: The Moderating Roles of Personality and the Mediating Role of Sleep on Job Performance and Satisfaction.

Yeh, Shu-Chuan Jennifer / Chen, Shih-Hua Sarah / Yuan, Kuo-Shu / Chou, Willy / Wan, Thomas T H

Frontiers in psychology

2020 Volume 11, Page(s) 574898

Abstract: The objective of this study is to investigate the effects of emotional labor on job performance and satisfaction, as well as to examine the mediating effect of sleep problems and the moderating effects of personality traits. A time-lagged study was ... ...

Abstract	The objective of this study is to investigate the effects of emotional labor on job performance and satisfaction, as well as to examine the mediating effect of sleep problems and the moderating effects of personality traits. A time-lagged study was conducted on 864 health professionals. Scales for emotional labor, sleep, personality traits, and job satisfaction were used and job performance data was obtained from records maintained by human resources. Structural equation modeling was performed to investigate the relations. Sleep problems only partially mediated the relationship between surface acting and job satisfaction but completely mediated the relationship between surface acting and job performance. Several personality traits were shown to moderate the relationship between surface acting and sleep problems. The effects were stronger for people with low agreeableness and high neuroticism. The relationship between high levels of deep acting and low levels of sleep problems was more pronounced in individuals with low extraversion. Supervisors should be conscious of emotional labor in the work context and provide necessary deep acting training to facilitate emotional regulation.
Language	English
Publishing date	2020-12-17
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2563826-9
ISSN	1664-1078
ISSN	1664-1078
DOI	10.3389/fpsyg.2020.574898
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Article: Breast Cancer Surgery 10-Year Survival Prediction by Machine Learning: A Large Prospective Cohort Study.

Lou, Shi-Jer / Hou, Ming-Feng / Chang, Hong-Tai / Lee, Hao-Hsien / Chiu, Chong-Chi / Yeh, Shu-Chuan Jennifer / Shi, Hon-Yi

Biology

2021 Volume 11, Issue 1

Abstract: Machine learning algorithms have proven to be effective for predicting survival after surgery, but their use for predicting 10-year survival after breast cancer surgery has not yet been discussed. This study compares the accuracy of predicting 10-year ... ...

Abstract	Machine learning algorithms have proven to be effective for predicting survival after surgery, but their use for predicting 10-year survival after breast cancer surgery has not yet been discussed. This study compares the accuracy of predicting 10-year survival after breast cancer surgery in the following five models: a deep neural network (DNN), K nearest neighbor (KNN), support vector machine (SVM), naive Bayes classifier (NBC) and Cox regression (COX), and to optimize the weighting of significant predictors. The subjects recruited for this study were breast cancer patients who had received breast cancer surgery (ICD-9 cm 174-174.9) at one of three southern Taiwan medical centers during the 3-year period from June 2007, to June 2010. The registry data for the patients were randomly allocated to three datasets, one for training (
Language	English
Publishing date	2021-12-29
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2661517-4
ISSN	2079-7737
ISSN	2079-7737
DOI	10.3390/biology11010047
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Article: Breast Cancer Surgery 10-Year Survival Prediction by Machine Learning: A Large Prospective Cohort Study

Lou, Shi-Jer / Hou, Ming-Feng / Chang, Hong-Tai / Lee, Hao-Hsien / Chiu, Chong-Chi / Yeh, Shu-Chuan Jennifer / Shi, Hon-Yi

Biology. 2021 Dec. 29, v. 11, no. 1

2021

Abstract	Machine learning algorithms have proven to be effective for predicting survival after surgery, but their use for predicting 10-year survival after breast cancer surgery has not yet been discussed. This study compares the accuracy of predicting 10-year survival after breast cancer surgery in the following five models: a deep neural network (DNN), K nearest neighbor (KNN), support vector machine (SVM), naive Bayes classifier (NBC) and Cox regression (COX), and to optimize the weighting of significant predictors. The subjects recruited for this study were breast cancer patients who had received breast cancer surgery (ICD-9 cm 174–174.9) at one of three southern Taiwan medical centers during the 3-year period from June 2007, to June 2010. The registry data for the patients were randomly allocated to three datasets, one for training (n = 824), one for testing (n = 177), and one for validation (n = 177). Prediction performance comparisons revealed that all performance indices for the DNN model were significantly (p < 0.001) higher than in the other forecasting models. Notably, the best predictor of 10-year survival after breast cancer surgery was the preoperative Physical Component Summary score on the SF-36. The next best predictors were the preoperative Mental Component Summary score on the SF-36, postoperative recurrence, and tumor stage. The deep-learning DNN model is the most clinically useful method to predict and to identify risk factors for 10-year survival after breast cancer surgery. Future research should explore designs for two-level or multi-level models that provide information on the contextual effects of the risk factors on breast cancer survival.
Keywords	Biological Sciences ; breast neoplasms ; cohort studies ; data collection ; models ; prediction ; regression analysis ; support vector machines ; surgery ; Taiwan
Language	English
Dates of publication	2021-1229
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2661517-4
ISSN	2079-7737
ISSN	2079-7737
DOI	10.3390/biology11010047
Database	NAL-Catalogue (AGRICOLA)

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Article: Predicting 30-Day Readmission for Stroke Using Machine Learning Algorithms: A Prospective Cohort Study.

Chen, Yu-Ching / Chung, Jo-Hsuan / Yeh, Yu-Jo / Lou, Shi-Jer / Lin, Hsiu-Fen / Lin, Ching-Huang / Hsien, Hong-Hsi / Hung, Kuo-Wei / Yeh, Shu-Chuan Jennifer / Shi, Hon-Yi

Frontiers in neurology

2022 Volume 13, Page(s) 875491

Abstract: Background: Machine learning algorithms for predicting 30-day stroke readmission are rarely discussed. The aims of this study were to identify significant predictors of 30-day readmission after stroke and to compare prediction accuracy and area under ... ...

Abstract	Background: Machine learning algorithms for predicting 30-day stroke readmission are rarely discussed. The aims of this study were to identify significant predictors of 30-day readmission after stroke and to compare prediction accuracy and area under the receiver operating characteristic (AUROC) curve in five models: artificial neural network (ANN), K nearest neighbor (KNN), random forest (RF), support vector machine (SVM), naive Bayes classifier (NBC), and Cox regression (COX) models. Methods: The subjects of this prospective cohort study were 1,476 patients with a history of admission for stroke to one of six hospitals between March, 2014, and September, 2019. A training dataset ( Results: For predicting 30-day readmission after stroke, the ANN model had significantly ( Conclusion: Using a machine-learning ANN model to obtain an accurate estimate of 30-day readmission for stroke and to identify risk factors may improve the precision and efficacy of management for these patients. For stroke patients who are candidates for PAC rehabilitation, these predictors have practical applications in educating patients in the expected course of recovery and health outcomes.
Language	English
Publishing date	2022-07-04
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2564214-5
ISSN	1664-2295
ISSN	1664-2295
DOI	10.3389/fneur.2022.875491
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