The conventional data envelopment analysis (DEA) measures the relative efficiencies of a set of decision making units with exact values of inputs and outputs. In real-world prob- lems, however, inputs and outputs ty...The conventional data envelopment analysis (DEA) measures the relative efficiencies of a set of decision making units with exact values of inputs and outputs. In real-world prob- lems, however, inputs and outputs typically have some levels of fuzziness. To analyze a decision making unit (DMU) with fuzzy input/output data, previous studies provided the fuzzy DEA model and proposed an associated evaluating approach. Nonetheless, numerous deficiencies must still be improved, including the α- cut approaches, types of fuzzy numbers, and ranking techniques. Moreover, a fuzzy sample DMU still cannot be evaluated for the Fuzzy DEA model. Therefore, this paper proposes a fuzzy DEA model based on sample decision making unit (FSDEA). Five eval- uation approaches and the related algorithm and ranking methods are provided to test the fuzzy sample DMU of the FSDEA model. A numerical experiment is used to demonstrate and compare the results with those obtained using alternative approaches.展开更多
Multi-professional collaboration is being promoted worldwide as a response to the need for sophisticated medical care and for catering to patients’ diverse needs. Patients in Intensive Care Units (ICUs) are seriously...Multi-professional collaboration is being promoted worldwide as a response to the need for sophisticated medical care and for catering to patients’ diverse needs. Patients in Intensive Care Units (ICUs) are seriously ill, and their families may be at risk depending on the patient’s situation. Considering these characteristics of patients and families, there is a strong need for multi-professional collaboration within ICUs. The purpose of this descriptive study was to examine recognition and other factors related to collaboration and satisfaction involving care decisions in Japanese ICUs. A mail survey about collaboration of activities and systems was sent to physicians, clinical engineers, and nurses working in ICUs in Japan, 387 consented to participate in this study. Results showed that satisfaction scores were generally high among the three aforementioned professions, but collaboration scores on deciding care for patients showed significant differences (p < 0.05). The total collaboration score was the highest among physicians (36.7 ± 6.7 points), followed by nurses (32.8 ± 7.4 points), and CEs (32.8 ± 7.4 points). The factors that commonly affected collaboration scores were the satisfaction score and the ability to collaborate with other professionals and set team medical care as a goal. Moreover, it is worth noting that the degree of difficulty in collaboration negatively affected this factor. On the other hand, other factors differed among the three professions, suggesting that the purpose and need for collaboration differ depending on the profession.展开更多
<strong>Background and Aim: </strong>Vigilance is an essential element in intensive care nursing. This study was conducted to determine nursing vigilance in nurses working in the intensive care units of ed...<strong>Background and Aim: </strong>Vigilance is an essential element in intensive care nursing. This study was conducted to determine nursing vigilance in nurses working in the intensive care units of educational and medical centers in Ardabil, Iran. <strong>Methods: </strong>This was a cross-sectional descriptive-analytical study with 192 ICU nurses as the participants. The data were obtained through questionnaires regarding demographic characteristics and nursing vigilance. SPSS software version 24 was used for the statistical analysis. <strong>Results: </strong>The mean total vigilance score was 3.86 ± 0.23 of 5. The mean scores of the timely diagnosis of changes, pattern recognition, and clinical decision-making subscales were 4.07 ± 0.26, 04.04 ± 0.41, and 3.44 ± 0.25, respectively. No significant relationships were observed between the total or subscale vigilance scores and other demographic characteristics. <strong>Conclusion: </strong>We assessed ICU nurses’ vigilance behaviors and found that their mean vigilance score was higher than the expected average, indicating our participants had a high level of clinical vigilance. These results suggest a need for effective educational interventions to boost clinical decision-making skills in ICU nurses, especially younger nurses.展开更多
目的调查某三甲儿童医院重症患儿照顾者决策疲劳现状,并分析其影响因素。方法采用便利抽样法,于2022年6月-2023年6月选取在儿童重症监护室(pediatric intensive care unit,PICU)住院治疗的118例重症患儿的照顾者作为研究对象,采用一般...目的调查某三甲儿童医院重症患儿照顾者决策疲劳现状,并分析其影响因素。方法采用便利抽样法,于2022年6月-2023年6月选取在儿童重症监护室(pediatric intensive care unit,PICU)住院治疗的118例重症患儿的照顾者作为研究对象,采用一般资料调查表、决策疲劳量表、家庭成员决策自我效能量表对其进行调查。结果该组重症患儿照顾者决策疲劳量表得分为(12.16±3.81)分,决策疲劳量表得分与家庭成员决策自我效能量表得分呈负相关(r=-0.882,P=0.012)。多元线性回归分析结果显示,重症患儿照顾者性别、患儿入住PICU天数、家庭平均月收入、决策自我效能是患儿照顾者决策疲劳的独立影响因素(R^(2)=0.952,_(△)R^(2)=0.902,F=16.819,P<0.001)。结论PICU重症患儿照顾者决策疲劳处于中等水平,医护人员应重点关注女性、家庭平均月收入低、患儿入住PICU时间长、决策自我效能水平低的患儿照顾者,及时向照顾者提供重症患儿病情、治疗方案、预后等信息,加强其心理支持,提升照顾者决策自我效能,从而降低其决策疲劳水平。展开更多
During efficiency evaluating by DEA, the inputs and outputs of DMUs may be intervals because of insufficient information or measure error. For this reason, interval DEA is proposed. To make the efficiency scores more ...During efficiency evaluating by DEA, the inputs and outputs of DMUs may be intervals because of insufficient information or measure error. For this reason, interval DEA is proposed. To make the efficiency scores more discriminative, this paper builds an Interval Modified DEA (IMDEA) model based on MDEA. Furthermore, models of obtaining upper and lower bounds of the efficiency scores for each DMU are set up. Based on this, the DMUs are classified into three types. Next, a new order relation between intervals which can express the DM’s preference to the three types is proposed. As a result, a full and more convictive ranking is made on all the DMUs. Finally an example is given.展开更多
To reduce flood losses,floodplain managers make decisions on how to effectively manage their community’s flood risks.While there is a growing body of research that examines how individuals and households make decisio...To reduce flood losses,floodplain managers make decisions on how to effectively manage their community’s flood risks.While there is a growing body of research that examines how individuals and households make decisions to manage their flood risks,far less attention has been directed at understanding the decision-making processes for flood management at the community level.This study aimed to narrow this research gap by examining floodplain managers’perceptions of the quality of their community’s flood management decision-making processes.Data gathered from interviews with 200 floodplain managers in the United States indicate that most floodplain managers perceive their community’s flood management decision-making processes to be good.The results also indicate that communities participating in the Federal Emergency Management Agency’s Community Rating System,as well as communities with a higher level of concern for flooding and a lower poverty rate,are significantly more likely to report better flood management decision-making processes.展开更多
The classic data envelopment analysis(DEA) model is used to evaluate decision-making units'(DMUs) efficiency under the assumption that all DMUs are evaluated with the same criteria setting. Recently, new research...The classic data envelopment analysis(DEA) model is used to evaluate decision-making units'(DMUs) efficiency under the assumption that all DMUs are evaluated with the same criteria setting. Recently, new researches begin to focus on the efficiency analysis of non-homogeneous DMU arose by real practices such as the evaluation of departments in a university, where departments argue for the adoption of different criteria based on their disciplinary characteristics. A DEA procedure is proposed in this paper to address the efficiency analysis of two non-homogeneous DMU groups. Firstly, an analytical framework is established to compromise diversified input and output(IO) criteria from two nonhomogenous groups. Then, a criteria fusion operation is designed to obtain different DEA analysis strategies. Meanwhile, Friedman test is introduced to analyze the consistency of all efficiency results produced by different strategies. Next, ordered weighted averaging(OWA) operators are applied to integrate different information to reach final conclusions. Finally, a numerical example is used to illustrate the proposed method. The result indicates that the proposed method relaxes the restriction of the classical DEA model,and can provide more analytical flexibility to address different decision analysis scenarios arose from practical applications.展开更多
基金supported by the National Natural Science Foundation of China (70961005)211 Project for Postgraduate Student Program of Inner Mongolia University+1 种基金National Natural Science Foundation of Inner Mongolia (2010Zd342011MS1002)
文摘The conventional data envelopment analysis (DEA) measures the relative efficiencies of a set of decision making units with exact values of inputs and outputs. In real-world prob- lems, however, inputs and outputs typically have some levels of fuzziness. To analyze a decision making unit (DMU) with fuzzy input/output data, previous studies provided the fuzzy DEA model and proposed an associated evaluating approach. Nonetheless, numerous deficiencies must still be improved, including the α- cut approaches, types of fuzzy numbers, and ranking techniques. Moreover, a fuzzy sample DMU still cannot be evaluated for the Fuzzy DEA model. Therefore, this paper proposes a fuzzy DEA model based on sample decision making unit (FSDEA). Five eval- uation approaches and the related algorithm and ranking methods are provided to test the fuzzy sample DMU of the FSDEA model. A numerical experiment is used to demonstrate and compare the results with those obtained using alternative approaches.
文摘Multi-professional collaboration is being promoted worldwide as a response to the need for sophisticated medical care and for catering to patients’ diverse needs. Patients in Intensive Care Units (ICUs) are seriously ill, and their families may be at risk depending on the patient’s situation. Considering these characteristics of patients and families, there is a strong need for multi-professional collaboration within ICUs. The purpose of this descriptive study was to examine recognition and other factors related to collaboration and satisfaction involving care decisions in Japanese ICUs. A mail survey about collaboration of activities and systems was sent to physicians, clinical engineers, and nurses working in ICUs in Japan, 387 consented to participate in this study. Results showed that satisfaction scores were generally high among the three aforementioned professions, but collaboration scores on deciding care for patients showed significant differences (p < 0.05). The total collaboration score was the highest among physicians (36.7 ± 6.7 points), followed by nurses (32.8 ± 7.4 points), and CEs (32.8 ± 7.4 points). The factors that commonly affected collaboration scores were the satisfaction score and the ability to collaborate with other professionals and set team medical care as a goal. Moreover, it is worth noting that the degree of difficulty in collaboration negatively affected this factor. On the other hand, other factors differed among the three professions, suggesting that the purpose and need for collaboration differ depending on the profession.
文摘<strong>Background and Aim: </strong>Vigilance is an essential element in intensive care nursing. This study was conducted to determine nursing vigilance in nurses working in the intensive care units of educational and medical centers in Ardabil, Iran. <strong>Methods: </strong>This was a cross-sectional descriptive-analytical study with 192 ICU nurses as the participants. The data were obtained through questionnaires regarding demographic characteristics and nursing vigilance. SPSS software version 24 was used for the statistical analysis. <strong>Results: </strong>The mean total vigilance score was 3.86 ± 0.23 of 5. The mean scores of the timely diagnosis of changes, pattern recognition, and clinical decision-making subscales were 4.07 ± 0.26, 04.04 ± 0.41, and 3.44 ± 0.25, respectively. No significant relationships were observed between the total or subscale vigilance scores and other demographic characteristics. <strong>Conclusion: </strong>We assessed ICU nurses’ vigilance behaviors and found that their mean vigilance score was higher than the expected average, indicating our participants had a high level of clinical vigilance. These results suggest a need for effective educational interventions to boost clinical decision-making skills in ICU nurses, especially younger nurses.
文摘目的调查某三甲儿童医院重症患儿照顾者决策疲劳现状,并分析其影响因素。方法采用便利抽样法,于2022年6月-2023年6月选取在儿童重症监护室(pediatric intensive care unit,PICU)住院治疗的118例重症患儿的照顾者作为研究对象,采用一般资料调查表、决策疲劳量表、家庭成员决策自我效能量表对其进行调查。结果该组重症患儿照顾者决策疲劳量表得分为(12.16±3.81)分,决策疲劳量表得分与家庭成员决策自我效能量表得分呈负相关(r=-0.882,P=0.012)。多元线性回归分析结果显示,重症患儿照顾者性别、患儿入住PICU天数、家庭平均月收入、决策自我效能是患儿照顾者决策疲劳的独立影响因素(R^(2)=0.952,_(△)R^(2)=0.902,F=16.819,P<0.001)。结论PICU重症患儿照顾者决策疲劳处于中等水平,医护人员应重点关注女性、家庭平均月收入低、患儿入住PICU时间长、决策自我效能水平低的患儿照顾者,及时向照顾者提供重症患儿病情、治疗方案、预后等信息,加强其心理支持,提升照顾者决策自我效能,从而降低其决策疲劳水平。
文摘During efficiency evaluating by DEA, the inputs and outputs of DMUs may be intervals because of insufficient information or measure error. For this reason, interval DEA is proposed. To make the efficiency scores more discriminative, this paper builds an Interval Modified DEA (IMDEA) model based on MDEA. Furthermore, models of obtaining upper and lower bounds of the efficiency scores for each DMU are set up. Based on this, the DMUs are classified into three types. Next, a new order relation between intervals which can express the DM’s preference to the three types is proposed. As a result, a full and more convictive ranking is made on all the DMUs. Finally an example is given.
基金funded by the US National Science Foundation(NSF)Grant No.1838421。
文摘To reduce flood losses,floodplain managers make decisions on how to effectively manage their community’s flood risks.While there is a growing body of research that examines how individuals and households make decisions to manage their flood risks,far less attention has been directed at understanding the decision-making processes for flood management at the community level.This study aimed to narrow this research gap by examining floodplain managers’perceptions of the quality of their community’s flood management decision-making processes.Data gathered from interviews with 200 floodplain managers in the United States indicate that most floodplain managers perceive their community’s flood management decision-making processes to be good.The results also indicate that communities participating in the Federal Emergency Management Agency’s Community Rating System,as well as communities with a higher level of concern for flooding and a lower poverty rate,are significantly more likely to report better flood management decision-making processes.
基金supported by the National Natural Science Foundation of China(71471087)
文摘The classic data envelopment analysis(DEA) model is used to evaluate decision-making units'(DMUs) efficiency under the assumption that all DMUs are evaluated with the same criteria setting. Recently, new researches begin to focus on the efficiency analysis of non-homogeneous DMU arose by real practices such as the evaluation of departments in a university, where departments argue for the adoption of different criteria based on their disciplinary characteristics. A DEA procedure is proposed in this paper to address the efficiency analysis of two non-homogeneous DMU groups. Firstly, an analytical framework is established to compromise diversified input and output(IO) criteria from two nonhomogenous groups. Then, a criteria fusion operation is designed to obtain different DEA analysis strategies. Meanwhile, Friedman test is introduced to analyze the consistency of all efficiency results produced by different strategies. Next, ordered weighted averaging(OWA) operators are applied to integrate different information to reach final conclusions. Finally, a numerical example is used to illustrate the proposed method. The result indicates that the proposed method relaxes the restriction of the classical DEA model,and can provide more analytical flexibility to address different decision analysis scenarios arose from practical applications.