Objectives This study aimed to explore and clarify the concept of reflective supervision as a professional self-care strategy to create a positive Intensive Care Unit(ICU)practice environment.Methods Walker and Avant...Objectives This study aimed to explore and clarify the concept of reflective supervision as a professional self-care strategy to create a positive Intensive Care Unit(ICU)practice environment.Methods Walker and Avant’s eight-step concept analysis approach was utilized to identify and define the attributes,antecedents,and consequences of reflective supervision in the ICU.An extensive literature search was conducted across various databases,including Google Scholar,CINAHL,PubMed.Articles published from 2005 to 2025 were identified.We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)2020 statement to indicate the included articles and extract related data based on relevance.Results Forty articles were included in the analysis.The identified attributes included the supervisor-supervisee relationship,effective communication,teamwork,collaborations,reflection,competencies,feedback,continuous support,and autonomous choice.The identified antecedents included participation,supportive supervision,flexibility,open-door policy,training,and motivation.Consequences impacting the success of reflective supervision were identified as promotion of resiliency,autonomy,work-life balance,self-awareness,increased self-esteem,professional development,critical thinking,increased job satisfaction,and enhanced commitment.Conclusions Reflective supervision is a complex professional self-care strategy that enhances ICU practice,by promoting nurses’well-being,self-awareness,therapeutic skills,and professional development.展开更多
Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental con...Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.展开更多
文摘Objectives This study aimed to explore and clarify the concept of reflective supervision as a professional self-care strategy to create a positive Intensive Care Unit(ICU)practice environment.Methods Walker and Avant’s eight-step concept analysis approach was utilized to identify and define the attributes,antecedents,and consequences of reflective supervision in the ICU.An extensive literature search was conducted across various databases,including Google Scholar,CINAHL,PubMed.Articles published from 2005 to 2025 were identified.We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)2020 statement to indicate the included articles and extract related data based on relevance.Results Forty articles were included in the analysis.The identified attributes included the supervisor-supervisee relationship,effective communication,teamwork,collaborations,reflection,competencies,feedback,continuous support,and autonomous choice.The identified antecedents included participation,supportive supervision,flexibility,open-door policy,training,and motivation.Consequences impacting the success of reflective supervision were identified as promotion of resiliency,autonomy,work-life balance,self-awareness,increased self-esteem,professional development,critical thinking,increased job satisfaction,and enhanced commitment.Conclusions Reflective supervision is a complex professional self-care strategy that enhances ICU practice,by promoting nurses’well-being,self-awareness,therapeutic skills,and professional development.
文摘Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.
