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Aims Our study investigates the influence of career facilitators and barriers on nurses’ improvement of their professional capabilities and their professional turnover intention. Background Reducing nurses’ professional turnover intention could help alleviate the global nursing shortage. Nevertheless, little research has addressed how career facilitators and barriers, nurses’ improvement of their professional capabilities and professional turnover intention are related, indicating a gap. Design This study used a cross‐sectional design. Methods We surveyed 502 out of 2,660 full‐time nurses who worked for a medical centre in Taiwan between January–March 2018. Our items were adapted from Cunningham et al. and Teng et al. and had adequate reliability and validity. Structural equation modelling was used to test the study hypotheses. Results Human capital, social capital, and discrimination were positively related to intention to improve professional capabilities. Moreover, intention to improve professional capabilities was positively related to action to improve professional capabilities, which was negatively related to professional turnover intention. Conclusion Most of the career facilitators and even barriers, boost the improvement of professional capabilities and are useful for retaining nurses in the nursing profession. Impact Findings of this study should have an impact on nursing managers by offering them means to retain nurses, for example, enhancing human capital and social capital among nurses to reduce their turnover intention.

The fifth generation (5G) mobile network delivers high peak data rates with ultra-low latency and massive network capacity. Wireless sensor network (WSN) in Internet of Thing (IoT) architecture is of prominent use in 5G-enabled applications. The electronic healthcare (e-health) system has gained a lot of research attention since it allows e-health users to store and share data in a convenient way. By the support of 5G technology, healthcare data produced by sensor nodes are transited in the e-health system with high efficiency and reliability. It helps in reducing the treatment cost, providing efficient services, better analysis reports, and faster access to treatment. However, security and privacy issues become big concerns when the number of sensors and mobile devices is increasing. Moreover, existing single-server architecture requires to store a massive number of identities and passwords, which causes a significant database cost. In this paper, we propose a three-factor fast authentication scheme with time bound and user anonymity for multi-server e-health systems in 5G-based wireless sensor networks. In our work, the three-factor authentication scheme integrating biometrics, password, and smart card ensures a high-security sensor-enabled environment for communicating parties. User anonymity is preserved during communication process. Besides, time bound authentication can be applied to various healthcare scenarios to enhance security. The proposed protocol includes fast authentication, which can provide a fast communication for participating parties. Our protocol is also designed with multi-server architecture to simplify network load and significantly save database cost. Furthermore, security proof and performance analysis results show that our proposed protocol can resist various attacks and bear a rational communication cost.

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BackgroundFatigue is a common symptom in the general population and has a substantial effect on individuals’ quality of life. The Multidimensional Fatigue Inventory (MFI) has been widely used to quantify the impact of fatigue, but no Traditional Chinese translation has yet been validated. The goal of this study was to translate the MFI from English into Traditional Chinese (‘the MFI-TC’) and subsequently to examine its validity and reliability.MethodsThe study recruited a convenience sample of 123 people from various age groups in Taiwan. The MFI was examined using a two-step process: (1) translation and back-translation of the instrument; and (2) examination of construct validity, convergent validity, internal consistency, test-retest reliability, and measurement error. The validity and reliability of the MFI-TC were assessed by factor analysis, Spearman rho correlation coefficient, Cronbach’s alpha coefficient, intraclass correlation coefficient (ICC), minimal detectable change (MDC), and Bland-Altman analysis. All participants completed the Short-Form-36 Health Survey Taiwan Form (SF-36-T) and the Chinese version of the Pittsburgh Sleep Quality Index (PSQI) concurrently to test the convergent validity of the MFI-TC. Test-retest reliability was assessed by readministration of the MFI-TC after a 1-week interval.ResultsFactor analysis confirmed the four dimensions of fatigue: general/physical fatigue, reduced activity, reduced motivation, and mental fatigue. A four-factor model was extracted, combining general fatigue and physical fatigue as one factor. The results demonstrated moderate convergent validity when correlating fatigue (MFI-TC) with quality of life (SF-36-T) and sleep disturbances (PSQI) (Spearman's rho = 0.68 and 0.47, respectively). Cronbach’s alpha for the MFI-TC total scale and subscales ranged from 0.73 (mental fatigue subscale) to 0.92 (MFI-TC total scale). ICCs ranged from 0.85 (reduced motivation) to 0.94 (MFI-TC total scale), and the MDC ranged from 2.33 points (mental fatigue) to 9.5 points (MFI-TC total scale). The Bland-Altman analyses showed no significant systematic bias between the repeated assessments.ConclusionsThe results support the use of the Traditional Chinese version of the MFI as a comprehensive instrument for measuring specific aspects of fatigue. Clinicians and researchers should consider interpreting general fatigue and physical fatigue as one subscale when measuring fatigue in Traditional Chinese-speaking populations.

Prediction of post-stroke functional outcomes is crucial for allocating medical resources. In this study, a total of 577 patients were enrolled in the Post-Acute Care-Cerebrovascular Disease (PAC-CVD) program, and 77 predictors were collected at admission. The outcome was whether a patient could achieve a Barthel Index (BI) score of >60 upon discharge. Eight machine-learning (ML) methods were applied, and their results were integrated by stacking method. The area under the curve (AUC) of the eight ML models ranged from 0.83 to 0.887, with random forest, stacking, logistic regression, and support vector machine demonstrating superior performance. The feature importance analysis indicated that the initial Berg Balance Test (BBS-I), initial BI (BI-I), and initial Concise Chinese Aphasia Test (CCAT-I) were the top three predictors of BI scores at discharge. The partial dependence plot (PDP) and individual conditional expectation (ICE) plot indicated that the predictors’ ability to predict outcomes was the most pronounced within a specific value range (e.g., BBS-I < 40 and BI-I < 60). BI at discharge could be predicted by information collected at admission with the aid of various ML models, and the PDP and ICE plots indicated that the predictors could predict outcomes at a certain value range.

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