Objective: The objective of this study was to reduce or avoid the occurrence of the cases of osteofascial compartment syndrome induced by a radial artery puncture for arterial blood gas analysis.Methods: We analyzed a...Objective: The objective of this study was to reduce or avoid the occurrence of the cases of osteofascial compartment syndrome induced by a radial artery puncture for arterial blood gas analysis.Methods: We analyzed an adverse event using cheese model analysis, "fish bone M analysis, root cause analysis, and other methods.Results: There are three root causes leading to an adverse event: operation technique, assessment of the disease, and informing patient families. However, there are many reasons to promote the occurrence and development of the event.O>ndusions: We should analyze and manage the adverse events in patients from the point of view of a system. Developing the measures of a system defense can enhance patient safety and create a good safety culture.展开更多
Root-cause identification plays a vital role in business decision making by providing effective future directions for the organizations.Aspect extraction and sentiment extraction plays a vital role in identifying the ...Root-cause identification plays a vital role in business decision making by providing effective future directions for the organizations.Aspect extraction and sentiment extraction plays a vital role in identifying the rootcauses.This paper proposes the Ensemble based temporal weighting and pareto ranking(ETP)model for Root-cause identification.Aspect extraction is performed based on rules and is followed by opinion identification using the proposed boosted ensemble model.The obtained aspects are validated and ranked using the proposed aspect weighing scheme.Pareto-rule based aspect selection is performed as the final selection mechanism and the results are presented for business decision making.Experiments were performed with the standard five product benchmark dataset.Performances on all five product reviews indicate the effective performance of the proposed model.Comparisons are performed using three standard state-of-the-art models and effectiveness is measured in terms of F-Measure and Detection rates.The results indicate improved performances exhibited by the proposed model with an increase in F-Measure levels at 1%–15%and detection rates at 4%–24%compared to the state-of-the-art models.展开更多
A distributed information network with complex network structure always has a challenge of locating fault root causes.In this paper,we propose a novel root cause analysis(RCA)method by random walk on the weighted faul...A distributed information network with complex network structure always has a challenge of locating fault root causes.In this paper,we propose a novel root cause analysis(RCA)method by random walk on the weighted fault propagation graph.Different from other RCA methods,it mines effective features information related to root causes from offline alarms.Combined with the information,online alarms and graph relationship of network structure are used to construct a weighted graph.Thus,this approach does not require operational experience and can be widely applied in different distributed networks.The proposed method can be used in multiple fault location cases.The experiment results show the proposed approach achieves much better performance with 6%higher precision at least for root fault location,compared with three baseline methods.Besides,we explain how the optimal parameter’s value in the random walk algorithm influences RCA results.展开更多
Modern industrial systems are usually in large scale,consisting of massive components and variables that form a complex system topology.Owing to the interconnections among devices,a fault may occur and propagate to ex...Modern industrial systems are usually in large scale,consisting of massive components and variables that form a complex system topology.Owing to the interconnections among devices,a fault may occur and propagate to exert widespread influences and lead to a variety of alarms.Obtaining the root causes of alarms is beneficial to the decision supports in making corrective alarm responses.Existing data-driven methods for alarm root cause analysis detect causal relations among alarms mainly based on historical alarm event data.To improve the accuracy,this paper proposes a causal fusion inference method for industrial alarm root cause analysis based on process topology and alarm events.A Granger causality inference method considering process topology is exploited to find out the causal relations among alarms.The topological nodes are used as the inputs of the model,and the alarm causal adjacency matrix between alarm variables is obtained by calculating the likelihood of the topological Hawkes process.The root cause is then obtained from the directed acyclic graph(DAG)among alarm variables.The effectiveness of the proposed method is verified by simulations based on both a numerical example and the Tennessee Eastman process(TEP)model.展开更多
A popular method to detect anomalous behaviour or specific failures in wind turbine sensor data uses a specific type of neural network called an autoencoder.These models have proven to be very successful in detecting ...A popular method to detect anomalous behaviour or specific failures in wind turbine sensor data uses a specific type of neural network called an autoencoder.These models have proven to be very successful in detecting such deviations,yet cannot show the underlying cause or failure directly.Such information is necessary for the implementation of these models in the planning of maintenance actions.In this paper we introduce a novel method:ARCANA.We use ARCANA to identify the possible root causes of anomalies detected by an autoencoder.It describes the process of reconstruction as an optimisation problem that aims to remove anomalous properties from an anomaly considerably.This reconstruction must be similar to the anomaly and thus identify only a few,but highly explanatory anomalous features,in the sense of Ockham’s razor.The proposed method is applied on an open data set of wind turbine sensor data,where an artificial error was added onto the wind speed sensor measurements to acquire a controlled test environment.The results are compared with the reconstruction errors of the autoencoder output.The ARCANA method points out the wind speed sensor correctly with a significantly higher feature importance than the other features,whereas using the non-optimised reconstruction error does not.Even though the deviation in one specific input feature is very large,the reconstruction error of many other features is large as well,complicating the interpretation of the detected anomaly.Additionally,we apply ARCANA to a set of offshore wind turbine data.Two case studies are discussed,demonstrating the technical relevance of ARCANA.展开更多
Objective:To explore the status of self-perceived burden(SPB)in primary glaucoma patients and to analyze its influencing factors.Subject and setting:A questionnaire survey was administered to 236 inpatients from a ter...Objective:To explore the status of self-perceived burden(SPB)in primary glaucoma patients and to analyze its influencing factors.Subject and setting:A questionnaire survey was administered to 236 inpatients from a tertiary general hospital and a eye hospital in Tianjin.The investigation was conducted after obtaining informed consent from each participant.Instruments:They were investigated using general data questionnaire,Self-Perceived Burden Scale(SPBS),Medical Coping Modes Questionnaire(MCMQ).Design:A descriptive cross-sectional design was used to gather data in this study.Results:The total SPBS score of primary glaucoma patients was(31.10±9.34)was medium.Regression consults showed that avoidance and surrender coping style,medical burden and right eye vision were the influencing factors of patients’SPB(P<0.05).Conclusion:Patients with primary glaucoma have a relatively heavy SPB,so medical staff should encourage them to actively face it.Tailored strategies in line with the patient’s economic and visual conditions to reduce the SPB.展开更多
Objective:This project has mainly studied the online learning engagement of undergraduate nursing students and analyzes influencing factors of online learning and teaching mode during the Novel Coronavirus(COVID-19).T...Objective:This project has mainly studied the online learning engagement of undergraduate nursing students and analyzes influencing factors of online learning and teaching mode during the Novel Coronavirus(COVID-19).This research has significant references for improving the efficiency and quality of the online learning mode of students.Methods:In this study,212 undergraduate nursing students were selected from a comprehensive university in Jilin Province by combining convenience sampling and cluster sampling methods.And these students were conducted with a general information questionnaire,Online Academic Emotion Scale,and Online Learning Engagement Scale.The influencing factors of this teaching mode were analyzed by multiple linear stepwise regression.Results:The total score of online learning engagement of undergraduate students was 53.85±7.38,which positively correlated with positive high arousal emotion and negative high arousal emotion,but weakly negatively correlated with negative low arousal emotion(r=0.661,0.246,-0.187,P<0.001).Grade,type of online class,online learning time,and positively high arousal emotion were mainly affected the online learning engagement of undergraduate nursing students,which explained 78.5%of the total variation(P<0.001).Conclusion:The online learning engagement of undergraduate nursing students was above the middle level under the background of the COVID-19 pandemic.Lectures and professors who teach undergraduate nursing students,should integrate the individuation characters of nursing students,and motivate their positively high arousal emotion to improve online learning engagement of students to ensure the quality of online teaching mode.展开更多
The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using su...The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using supervision platforms that generate alarms that can be archived in the form of log files. But analyzing the alarms in the log files is a laborious and difficult task for the engineers who need a degree of expertise. Identifying failures and their root cause can be time consuming and impact the quality of service, network availability and service level agreements signed between the operator and its customers. Therefore, it is more than important to study the different possibilities of alarms classification and to use machine learning algorithms for alarms correlation in order to quickly determine the root causes of problems faster. We conducted a research case study on one of the operators in Cameroon who held an optical backbone based on SDH and WDM technologies with data collected from 2016-03-28 to “2022-09-01” with 7201 rows and 18. In this paper, we will classify alarms according to different criteria and use 02 unsupervised learning algorithms namely the K-Means algorithm and the DBSCAN to establish correlations between alarms in order to identify root causes of problems and reduce the time to troubleshoot. To achieve this objective, log files were exploited in order to obtain the root causes of the alarms, and then K-Means algorithm and the DBSCAN were used firstly to evaluate their performance and their capability to identify the root cause of alarms in optical network.展开更多
Background:Nurses'turnover has been a major concern globally,which is strongly influenced by nurses'intent to leave.However,only a few large sample studies on the predictive factors associated with nurses'...Background:Nurses'turnover has been a major concern globally,which is strongly influenced by nurses'intent to leave.However,only a few large sample studies on the predictive factors associated with nurses'turnover intention were conducted in Jiangsu Province.This study mainly aims to examine the level and factors that influence nurses to leave their work in Jiangsu Province of Eastern China.Methods:A cross-sectional survey of 1978 nurses was conducted at 48 hospitals in 14 key cities throughout Jiangsu Province.The turnover intention in nurses was measured by the scale of intent to leave the profession.The work environment of nurses was measured by the Chinese version of the Practice Environment Scale.A multiple linear regression model was applied to analyse the factors associated with turnover intention.Results:The resignation rate of nurses in the hospitals of Jiangsu Province ranged from 0.64%to 12.71%in 2016.The mean scores were 15.50±3.44 for turnover intention,and 3.06±0.51 for work environment.Involvement in hospital affairs,resource adequacy,age,professional title,year(s)working,employment type and education level were the predictors of nurse intent to leave(P<0.05).Conclusion:The work environment of nurses in hospitals must be improved in staffing and resource and nurses'involvement in hospital affairs.The current study corroborates that nurses have high turnover intention.Thus,effective measures are needed to improve nurse accomplishment,professional status,participation in hospital affairs and career planning to reduce their turnover intention.展开更多
Objectives: This study aims to construct a theoretical framework to analyze risk factors and explore hospital nurses' perspectives on care complexity.Methods: The grounded theory method was adopted,and semi-struct...Objectives: This study aims to construct a theoretical framework to analyze risk factors and explore hospital nurses' perspectives on care complexity.Methods: The grounded theory method was adopted,and semi-structured in-depth interviews regarding the understanding of care complexity were conducted among the participants,including 31 nurses and nine doctors.In addition,data were coded and strictly analyzed in accordance with the coding strategy and requirements of grounded theory.Results: Our study reveals three factors that are closely related to care complexity,namely,(1) patient factors,including patients' condition,age,self-care abilities,compliance,social support systems,psy chological conditions,expectations,and requirements;(2) nursing staff factors,including work experiences,education,knowledge and operational skills of caring,and communication skills;and (3) organization and equipment factors,including nursing workforce,nursing workload,support from multidisciplinary teams and ancillary departments,and the conditions of medical and hospital services.Conclusions: This study defines care complexity on the basis of its factors.Care complexity refers to the difficulty of nursing tasks during patient care plan implementation,which are affected by patients,nurses,and other factors in nursing and multisectoral,multidisciplinary cooperation.The framework can be beneficial for nursing education and for the improvement of the quality and efficiency of clinical nursing practice.展开更多
Background:This study aimed to determine the prevalence and predictive factors of prolonged grief disorder(PGD)among those bereaved by the Wenchuan earthquake in Southwestern China seven years after the event.Methods:...Background:This study aimed to determine the prevalence and predictive factors of prolonged grief disorder(PGD)among those bereaved by the Wenchuan earthquake in Southwestern China seven years after the event.Methods:A cross-sectional survey based on census tracts was conducted on the bereaved earthquake survivors.Responses to the questionnaire regarding PGD and its potential associated factors were obtained either through face-to-face or telephone interview.PGD was screened by a validated Chinese version of the PGD questionnaire-13(PG-13).Bivariate and multivariate regression analyses were used to determine the prevalence and associated risk factors of PGD.Results:A total of 1464 bereaved earthquake survivors,with a response rate of 97.6%,were included in the study.Of the 1464 respondents studied,124(8.47%)were diagnosed with PGD.Multivariate regression analysis demonstrated that PGD in the bereaved earthquake individuals was significantly associated with several factors,including age,economic burden,close kinship with the deceased,and living with the deceased before the loss.Wenchuan earthquake bereaved aged 41e60 years were more likely to develop PGD compared to those aged younger than 40 or older than 60(OR=2.075,95%CI=1.297e3.319).Those who had a close kinship with the deceased had a higher tendency to develop PGD(OR=5.144,95%CI=2.716e9.740).The odds of PGD among the earthquake bereaved with economic burdens were higher relative to those who did not experience an economic burden(OR=8.123,95%CI=2.657e24.831).Those who living with the deceased before loss also had a higher tendency to develop PGD(OR=0.179,95%CI=0.053e0.602).Conclusions:This study revealed that a significantly high proportion(8.47%)of the Wenchuan earthquake-bereaved remain grieving seven years after the event.Those diagnosed with PGD should receive appropriate interventions from clinical psychologists.The risk factors identified in this study are crucial for the early screening and prevention of PGD in future nursing and psycho-clinical practices.展开更多
Background: As medical technology has advanced, it has also made it possible to maintain end-stage life support for longer periods of time, but it has also been accompanied by a debate about ineffective care, nursing ...Background: As medical technology has advanced, it has also made it possible to maintain end-stage life support for longer periods of time, but it has also been accompanied by a debate about ineffective care, nursing is considered to be an ethically important profession, and nurses aim to achieve ethical goals such as providing the best possible care to patients, achieving high quality outcomes, but it is common when there are insufficient numbers of staff, inadequately trained staff, and organizational policies and procedures that make it difficult, or even impossible, for nurses to meet the needs of patients and their families. This conflict results in moral distress for nurses, yet limited attention has been paid to this phenomenon. Objective: To explore the current phenomenon of moral distress and its triggering factors in nurses in tertiary grade A hospitals in Wuhan, by targeting root causes and understanding the interplay between nurses and settings where moral distress occurs, interventions can be tailored to minimize moral distress with the ultimate goal of enhancing patient care. Method: Totally 384 nurses from clinical departments in 2 tertiary Grade A hospitals in Wuhan were investigated with the Chinese version Moral Distress Scale-Revised (MDS-R). Result: The total score of moral distress was 47.41 ± 27.14, and the mean scores of moral distress frequency and intensity were 1.01 ± 0.53 and 1.19 ± 0.61, which were at a lower level. The main source of moral distress for nurses is related to end-of-life care and medical decision communication;Nurses’ moral distress scores were statistically significant (P Conclusion: Hospital facility leaders and nursing managers need to train nurses to develop competency development such as reflection, empathy, communication, positive thinking, and emotional intelligence to practice ethical dilemma response, and facilitate collaborative communication among healthcare members, so as to alleviate moral distress in nurses.展开更多
For efficient use of value stream mapping(VSM)for multi-varieties and small batch production in a data-rich environment enabled by Industry 4.0 technologies,a systematic framework of VSM to rejuvenate traditional lean...For efficient use of value stream mapping(VSM)for multi-varieties and small batch production in a data-rich environment enabled by Industry 4.0 technologies,a systematic framework of VSM to rejuvenate traditional lean tools is proposed.It addresses the issue that traditional VSM requires intensive on-site investigation and replies on experience,which hinders decisionmaking efficiency in dynamic and complex environments.The proposed framework follows the data-information-knowledge hierarchy model,and demonstrates how data can be collected in a production workshop,processed into information,and then interpreted into knowledge.In this paper,the necessity and limitations of VSM in automated root cause analysis are first discussed,with a literature review on lean production tools,especially VSM and VSM-based decision making in Industry 4.0.An implementation case of a furniture manufacturer in China is presented,where decision tree algorithm was used for automated root cause analysis.The results indicate that automated VSM can make good use of production data to cater for multi-varieties and small batch production with timely on-site waste identification and analysis.The proposed framework is also suggested as a guideline to renew other lean tools for reliable and efficient decision-making.展开更多
文摘Objective: The objective of this study was to reduce or avoid the occurrence of the cases of osteofascial compartment syndrome induced by a radial artery puncture for arterial blood gas analysis.Methods: We analyzed an adverse event using cheese model analysis, "fish bone M analysis, root cause analysis, and other methods.Results: There are three root causes leading to an adverse event: operation technique, assessment of the disease, and informing patient families. However, there are many reasons to promote the occurrence and development of the event.O>ndusions: We should analyze and manage the adverse events in patients from the point of view of a system. Developing the measures of a system defense can enhance patient safety and create a good safety culture.
文摘Root-cause identification plays a vital role in business decision making by providing effective future directions for the organizations.Aspect extraction and sentiment extraction plays a vital role in identifying the rootcauses.This paper proposes the Ensemble based temporal weighting and pareto ranking(ETP)model for Root-cause identification.Aspect extraction is performed based on rules and is followed by opinion identification using the proposed boosted ensemble model.The obtained aspects are validated and ranked using the proposed aspect weighing scheme.Pareto-rule based aspect selection is performed as the final selection mechanism and the results are presented for business decision making.Experiments were performed with the standard five product benchmark dataset.Performances on all five product reviews indicate the effective performance of the proposed model.Comparisons are performed using three standard state-of-the-art models and effectiveness is measured in terms of F-Measure and Detection rates.The results indicate improved performances exhibited by the proposed model with an increase in F-Measure levels at 1%–15%and detection rates at 4%–24%compared to the state-of-the-art models.
基金supported by ZTE Industry-University-Institute Cooperation Funds under Grant No.HC-CN-20201120009。
文摘A distributed information network with complex network structure always has a challenge of locating fault root causes.In this paper,we propose a novel root cause analysis(RCA)method by random walk on the weighted fault propagation graph.Different from other RCA methods,it mines effective features information related to root causes from offline alarms.Combined with the information,online alarms and graph relationship of network structure are used to construct a weighted graph.Thus,this approach does not require operational experience and can be widely applied in different distributed networks.The proposed method can be used in multiple fault location cases.The experiment results show the proposed approach achieves much better performance with 6%higher precision at least for root fault location,compared with three baseline methods.Besides,we explain how the optimal parameter’s value in the random walk algorithm influences RCA results.
基金supported by the National Natural Science Foundation of China(Nos.61903345 and 61973287)。
文摘Modern industrial systems are usually in large scale,consisting of massive components and variables that form a complex system topology.Owing to the interconnections among devices,a fault may occur and propagate to exert widespread influences and lead to a variety of alarms.Obtaining the root causes of alarms is beneficial to the decision supports in making corrective alarm responses.Existing data-driven methods for alarm root cause analysis detect causal relations among alarms mainly based on historical alarm event data.To improve the accuracy,this paper proposes a causal fusion inference method for industrial alarm root cause analysis based on process topology and alarm events.A Granger causality inference method considering process topology is exploited to find out the causal relations among alarms.The topological nodes are used as the inputs of the model,and the alarm causal adjacency matrix between alarm variables is obtained by calculating the likelihood of the topological Hawkes process.The root cause is then obtained from the directed acyclic graph(DAG)among alarm variables.The effectiveness of the proposed method is verified by simulations based on both a numerical example and the Tennessee Eastman process(TEP)model.
文摘A popular method to detect anomalous behaviour or specific failures in wind turbine sensor data uses a specific type of neural network called an autoencoder.These models have proven to be very successful in detecting such deviations,yet cannot show the underlying cause or failure directly.Such information is necessary for the implementation of these models in the planning of maintenance actions.In this paper we introduce a novel method:ARCANA.We use ARCANA to identify the possible root causes of anomalies detected by an autoencoder.It describes the process of reconstruction as an optimisation problem that aims to remove anomalous properties from an anomaly considerably.This reconstruction must be similar to the anomaly and thus identify only a few,but highly explanatory anomalous features,in the sense of Ockham’s razor.The proposed method is applied on an open data set of wind turbine sensor data,where an artificial error was added onto the wind speed sensor measurements to acquire a controlled test environment.The results are compared with the reconstruction errors of the autoencoder output.The ARCANA method points out the wind speed sensor correctly with a significantly higher feature importance than the other features,whereas using the non-optimised reconstruction error does not.Even though the deviation in one specific input feature is very large,the reconstruction error of many other features is large as well,complicating the interpretation of the detected anomaly.Additionally,we apply ARCANA to a set of offshore wind turbine data.Two case studies are discussed,demonstrating the technical relevance of ARCANA.
文摘Objective:To explore the status of self-perceived burden(SPB)in primary glaucoma patients and to analyze its influencing factors.Subject and setting:A questionnaire survey was administered to 236 inpatients from a tertiary general hospital and a eye hospital in Tianjin.The investigation was conducted after obtaining informed consent from each participant.Instruments:They were investigated using general data questionnaire,Self-Perceived Burden Scale(SPBS),Medical Coping Modes Questionnaire(MCMQ).Design:A descriptive cross-sectional design was used to gather data in this study.Results:The total SPBS score of primary glaucoma patients was(31.10±9.34)was medium.Regression consults showed that avoidance and surrender coping style,medical burden and right eye vision were the influencing factors of patients’SPB(P<0.05).Conclusion:Patients with primary glaucoma have a relatively heavy SPB,so medical staff should encourage them to actively face it.Tailored strategies in line with the patient’s economic and visual conditions to reduce the SPB.
文摘Objective:This project has mainly studied the online learning engagement of undergraduate nursing students and analyzes influencing factors of online learning and teaching mode during the Novel Coronavirus(COVID-19).This research has significant references for improving the efficiency and quality of the online learning mode of students.Methods:In this study,212 undergraduate nursing students were selected from a comprehensive university in Jilin Province by combining convenience sampling and cluster sampling methods.And these students were conducted with a general information questionnaire,Online Academic Emotion Scale,and Online Learning Engagement Scale.The influencing factors of this teaching mode were analyzed by multiple linear stepwise regression.Results:The total score of online learning engagement of undergraduate students was 53.85±7.38,which positively correlated with positive high arousal emotion and negative high arousal emotion,but weakly negatively correlated with negative low arousal emotion(r=0.661,0.246,-0.187,P<0.001).Grade,type of online class,online learning time,and positively high arousal emotion were mainly affected the online learning engagement of undergraduate nursing students,which explained 78.5%of the total variation(P<0.001).Conclusion:The online learning engagement of undergraduate nursing students was above the middle level under the background of the COVID-19 pandemic.Lectures and professors who teach undergraduate nursing students,should integrate the individuation characters of nursing students,and motivate their positively high arousal emotion to improve online learning engagement of students to ensure the quality of online teaching mode.
文摘The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using supervision platforms that generate alarms that can be archived in the form of log files. But analyzing the alarms in the log files is a laborious and difficult task for the engineers who need a degree of expertise. Identifying failures and their root cause can be time consuming and impact the quality of service, network availability and service level agreements signed between the operator and its customers. Therefore, it is more than important to study the different possibilities of alarms classification and to use machine learning algorithms for alarms correlation in order to quickly determine the root causes of problems faster. We conducted a research case study on one of the operators in Cameroon who held an optical backbone based on SDH and WDM technologies with data collected from 2016-03-28 to “2022-09-01” with 7201 rows and 18. In this paper, we will classify alarms according to different criteria and use 02 unsupervised learning algorithms namely the K-Means algorithm and the DBSCAN to establish correlations between alarms in order to identify root causes of problems and reduce the time to troubleshoot. To achieve this objective, log files were exploited in order to obtain the root causes of the alarms, and then K-Means algorithm and the DBSCAN were used firstly to evaluate their performance and their capability to identify the root cause of alarms in optical network.
基金This study was supported by the Jiangsu Provincial Health and Family Planning Commission(WSGL201605)
文摘Background:Nurses'turnover has been a major concern globally,which is strongly influenced by nurses'intent to leave.However,only a few large sample studies on the predictive factors associated with nurses'turnover intention were conducted in Jiangsu Province.This study mainly aims to examine the level and factors that influence nurses to leave their work in Jiangsu Province of Eastern China.Methods:A cross-sectional survey of 1978 nurses was conducted at 48 hospitals in 14 key cities throughout Jiangsu Province.The turnover intention in nurses was measured by the scale of intent to leave the profession.The work environment of nurses was measured by the Chinese version of the Practice Environment Scale.A multiple linear regression model was applied to analyse the factors associated with turnover intention.Results:The resignation rate of nurses in the hospitals of Jiangsu Province ranged from 0.64%to 12.71%in 2016.The mean scores were 15.50±3.44 for turnover intention,and 3.06±0.51 for work environment.Involvement in hospital affairs,resource adequacy,age,professional title,year(s)working,employment type and education level were the predictors of nurse intent to leave(P<0.05).Conclusion:The work environment of nurses in hospitals must be improved in staffing and resource and nurses'involvement in hospital affairs.The current study corroborates that nurses have high turnover intention.Thus,effective measures are needed to improve nurse accomplishment,professional status,participation in hospital affairs and career planning to reduce their turnover intention.
基金This research was supported by a grant from the Young Talents Training Project of Health Systems Support Program in Fujian Province,China(No.2013-ZQN-ZD-5)
文摘Objectives: This study aims to construct a theoretical framework to analyze risk factors and explore hospital nurses' perspectives on care complexity.Methods: The grounded theory method was adopted,and semi-structured in-depth interviews regarding the understanding of care complexity were conducted among the participants,including 31 nurses and nine doctors.In addition,data were coded and strictly analyzed in accordance with the coding strategy and requirements of grounded theory.Results: Our study reveals three factors that are closely related to care complexity,namely,(1) patient factors,including patients' condition,age,self-care abilities,compliance,social support systems,psy chological conditions,expectations,and requirements;(2) nursing staff factors,including work experiences,education,knowledge and operational skills of caring,and communication skills;and (3) organization and equipment factors,including nursing workforce,nursing workload,support from multidisciplinary teams and ancillary departments,and the conditions of medical and hospital services.Conclusions: This study defines care complexity on the basis of its factors.Care complexity refers to the difficulty of nursing tasks during patient care plan implementation,which are affected by patients,nurses,and other factors in nursing and multisectoral,multidisciplinary cooperation.The framework can be beneficial for nursing education and for the improvement of the quality and efficiency of clinical nursing practice.
基金This work was supported by funding from the Chengdu University of Traditional Chinese Medicine(Grant no:RWQN1410).
文摘Background:This study aimed to determine the prevalence and predictive factors of prolonged grief disorder(PGD)among those bereaved by the Wenchuan earthquake in Southwestern China seven years after the event.Methods:A cross-sectional survey based on census tracts was conducted on the bereaved earthquake survivors.Responses to the questionnaire regarding PGD and its potential associated factors were obtained either through face-to-face or telephone interview.PGD was screened by a validated Chinese version of the PGD questionnaire-13(PG-13).Bivariate and multivariate regression analyses were used to determine the prevalence and associated risk factors of PGD.Results:A total of 1464 bereaved earthquake survivors,with a response rate of 97.6%,were included in the study.Of the 1464 respondents studied,124(8.47%)were diagnosed with PGD.Multivariate regression analysis demonstrated that PGD in the bereaved earthquake individuals was significantly associated with several factors,including age,economic burden,close kinship with the deceased,and living with the deceased before the loss.Wenchuan earthquake bereaved aged 41e60 years were more likely to develop PGD compared to those aged younger than 40 or older than 60(OR=2.075,95%CI=1.297e3.319).Those who had a close kinship with the deceased had a higher tendency to develop PGD(OR=5.144,95%CI=2.716e9.740).The odds of PGD among the earthquake bereaved with economic burdens were higher relative to those who did not experience an economic burden(OR=8.123,95%CI=2.657e24.831).Those who living with the deceased before loss also had a higher tendency to develop PGD(OR=0.179,95%CI=0.053e0.602).Conclusions:This study revealed that a significantly high proportion(8.47%)of the Wenchuan earthquake-bereaved remain grieving seven years after the event.Those diagnosed with PGD should receive appropriate interventions from clinical psychologists.The risk factors identified in this study are crucial for the early screening and prevention of PGD in future nursing and psycho-clinical practices.
文摘Background: As medical technology has advanced, it has also made it possible to maintain end-stage life support for longer periods of time, but it has also been accompanied by a debate about ineffective care, nursing is considered to be an ethically important profession, and nurses aim to achieve ethical goals such as providing the best possible care to patients, achieving high quality outcomes, but it is common when there are insufficient numbers of staff, inadequately trained staff, and organizational policies and procedures that make it difficult, or even impossible, for nurses to meet the needs of patients and their families. This conflict results in moral distress for nurses, yet limited attention has been paid to this phenomenon. Objective: To explore the current phenomenon of moral distress and its triggering factors in nurses in tertiary grade A hospitals in Wuhan, by targeting root causes and understanding the interplay between nurses and settings where moral distress occurs, interventions can be tailored to minimize moral distress with the ultimate goal of enhancing patient care. Method: Totally 384 nurses from clinical departments in 2 tertiary Grade A hospitals in Wuhan were investigated with the Chinese version Moral Distress Scale-Revised (MDS-R). Result: The total score of moral distress was 47.41 ± 27.14, and the mean scores of moral distress frequency and intensity were 1.01 ± 0.53 and 1.19 ± 0.61, which were at a lower level. The main source of moral distress for nurses is related to end-of-life care and medical decision communication;Nurses’ moral distress scores were statistically significant (P Conclusion: Hospital facility leaders and nursing managers need to train nurses to develop competency development such as reflection, empathy, communication, positive thinking, and emotional intelligence to practice ethical dilemma response, and facilitate collaborative communication among healthcare members, so as to alleviate moral distress in nurses.
基金Project supported by the National Natural Science Foundation of China(Nos.72071179 and 51805479)the Natural Science Foundation of Zhejiang Province(No.LY19E050019)the Ministry of Industry and Information Technology of China(No.Z135060009002)。
文摘For efficient use of value stream mapping(VSM)for multi-varieties and small batch production in a data-rich environment enabled by Industry 4.0 technologies,a systematic framework of VSM to rejuvenate traditional lean tools is proposed.It addresses the issue that traditional VSM requires intensive on-site investigation and replies on experience,which hinders decisionmaking efficiency in dynamic and complex environments.The proposed framework follows the data-information-knowledge hierarchy model,and demonstrates how data can be collected in a production workshop,processed into information,and then interpreted into knowledge.In this paper,the necessity and limitations of VSM in automated root cause analysis are first discussed,with a literature review on lean production tools,especially VSM and VSM-based decision making in Industry 4.0.An implementation case of a furniture manufacturer in China is presented,where decision tree algorithm was used for automated root cause analysis.The results indicate that automated VSM can make good use of production data to cater for multi-varieties and small batch production with timely on-site waste identification and analysis.The proposed framework is also suggested as a guideline to renew other lean tools for reliable and efficient decision-making.
