Background In remote areas, connected health (CH) is needed, but as local resources are often scarce and the purchasing power of residents is usually poor, it is a challenge to apply CH in these settings. In this study, CH is defended as a technological solution for reshaping the direction of health care to be more proactive, preventive, and precisely targeted—and thus, more effective. Objective The objective of this study was to explore the identity of CH stakeholders in remote areas of Taiwan and their interests and power in order to determine ideal strategies for applying CH. We aimed to explore the respective unknowns and discover insights for those facing similar issues. Methods Qualitative research was conducted to investigate and interpret the phenomena of the aging population in a remote setting. An exploratory approach was employed involving semistructured interviews with 22 participants from 8 remote allied case studies. The interviews explored perspectives on stakeholder arrangements, including the power and interests of stakeholders and the needs of all the parties in the ecosystem. Results Results were obtained from in-depth interviews and focus groups that included identifying the stakeholders of remote health and determining how they influence its practice, as well as how associated agreements bring competitive advantages. Stakeholders included people in government sectors, industrial players, academic researchers, end users, and their associates who described their perspectives on their power and interests in remote health service delivery. Specific facilitators of and barriers to effective delivery were identified. A number of themes, such as government interests and power of decision making, were corroborated across rural and remote services. These themes were broadly grouped into the disclosure of conflicts of interest, asymmetry in decision making, and data development for risk assessment. Conclusions This study contributes to current knowledge by exploring the features of CH in remote areas and investigating its implementation from the perspectives of stakeholder management. It offers insights into managing remote health through a CH platform, which can be used for preliminary quantitative research. Consequently, these findings could help to more effectively facilitate diverse stakeholder engagement for health information sharing and social interaction.
PurposeThis study aims to explore the relative importance of the subdimensions of total rewards satisfaction in predicting research and development (R&D) employee creativity. In addition, the study examines the indirect effects of the subdimensions of total rewards satisfaction on creativity via work engagement and the moderating role of challenge-related work stress in the first stage.Design/methodology/approachA two-wave design was used, in which total rewards satisfaction and challenge-related work stress were measured in the first wave. Work engagement and creativity were measured in the second wave. Dominance analysis and the latent moderated mediation model were used for the data analyses.FindingsThe analyses show that nonfinancial rewards satisfaction completely dominates indirect and direct financial rewards satisfaction when predicting creativity. Indirect financial rewards satisfaction completely dominates direct financial rewards satisfaction when predicting creativity. Work engagement mediates the relationships between the subdimensions of total rewards satisfaction and creativity. Challenge-related work stress moderates the relationships between the subdimensions of total rewards satisfaction and work engagement and the indirect effects of the subdimensions of total rewards satisfaction on creativity via work engagement.Practical implicationsThe results imply that managers should set challenge demands for R&D employees and try to improve their total rewards satisfaction, especially their nonfinancial and indirect financial rewards satisfaction, for them to be more creative.Originality/valueThis empirical study contributes to the literature by comparing the relative importance of the different dimensions of total rewards satisfaction in predicting creativity. The study also clarifies how (through work engagement) and when (based on challenge-related work stress) the subdimensions of total rewards satisfaction are positively related to R&D employees' creativity.
PurposeClass imbalance learning, which exists in many domain problem datasets, is an important research topic in data mining and machine learning. One-class classification techniques, which aim to identify anomalies as the minority class from the normal data as the majority class, are one representative solution for class imbalanced datasets. Since one-class classifiers are trained using only normal data to create a decision boundary for later anomaly detection, the quality of the training set, i.e. the majority class, is one key factor that affects the performance of one-class classifiers.Design/methodology/approachIn this paper, we focus on two data cleaning or preprocessing methods to address class imbalanced datasets. The first method examines whether performing instance selection to remove some noisy data from the majority class can improve the performance of one-class classifiers. The second method combines instance selection and missing value imputation, where the latter is used to handle incomplete datasets that contain missing values.FindingsThe experimental results are based on 44 class imbalanced datasets; three instance selection algorithms, including IB3, DROP3 and the GA, the CART decision tree for missing value imputation, and three one-class classifiers, which include OCSVM, IFOREST and LOF, show that if the instance selection algorithm is carefully chosen, performing this step could improve the quality of the training data, which makes one-class classifiers outperform the baselines without instance selection. Moreover, when class imbalanced datasets contain some missing values, combining missing value imputation and instance selection, regardless of which step is first performed, can maintain similar data quality as datasets without missing values.Originality/valueThe novelty of this paper is to investigate the effect of performing instance selection on the performance of one-class classifiers, which has never been done before. Moreover, this study is the first attempt to consider the scenario of missing values that exist in the training set for training one-class classifiers. In this case, performing missing value imputation and instance selection with different orders are compared.
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