数据挖掘技术对放射工作人员知觉压力因素分析
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摘要
目的
知觉压力对个体身心健康有很大影响,普遍存在于不同职业的人群中,放射工作人群作为一种特定类型的职业群体,也不例外,该人群的心理健康的研究大多因素较单一,传统的分析方法存在缺陷。该研究通过分析多个因素对放射工作人员知觉压力的影响,揭示该人群心理健康、生活事件等因素在压力事件中的交互作用及量化关系,为数据挖掘技术应用于健康心理学的研究提供新思路。
对象与方法
对象来自于2007年4月在河南省职业病防治研究所进行体检的放射工作人员。资料为人口学调查以及心理学方面测量的量表,量表包括一般健康量表(CHQ-12)、艾森克人格简式量表(EPQ-RSC)、知觉压力量表(CPSS)、工作倦怠量表(MBI-GS)、生活事件量表(LES)、特质应对方式量表(TCSQ)、社会支持评定量表(SSS)。利用数据挖掘对资料进行分析,数据预处理,各指标测量分值分别与压力总分进行相关性分析和多重回归分析。选择相关指标,建立决策树模型,并对模型进行评价。数据分析在SPSS Clementinell.1中实现。
结果
1.根据相关性分析和多重回归分析结果显示,情绪耗竭、消极怠慢、精神质、消极应对和社会主观支持等因素对知觉压力有较大影响,选择相关指标,建立决策树模型。
2.决策树模型中将知觉压力作为因变量,筛选有统计意义的自变量,按照等级排序分别为：情绪耗竭、消极怠慢、积极应对、主观支持、精神质、正性生活刺激、消极应对、专业效能感,共筛选8个自变量。
3.模型按照是否产生知觉压力得到了11条分类,部分规则如下：
未产生知觉压力的规则：(1)负性生活事件刺激量≤3,专业效能感≤22,消极应对≤25,正性生活事件刺激量≤11,精神质≤3,主观支持≤28,积极应对≤34,消极怠慢≤5,情绪耗竭≤12；(2)主观支持>28,积极应对≤34,消极怠慢≤5,情绪耗竭≤12；(3)积极应对>34,消极怠慢≤5,情绪耗竭≤12。
产生知觉压力的规则：(1)消极应对>25,正性生活事件刺激量≤11,精神质≤3,主观支持≤28,积极应对≤34,消极怠慢≤5,情绪耗竭≤12；(2)精神质≤3,正性生活事件刺激量>11,主观支持≤28,积极应对≤34,消极怠慢≤5,情绪耗竭≤12；(3)消极怠慢>5,情绪耗竭≤12。
4.利用训练集和测试集拟合和训练模型,RISK统计量和一致性检验测试模型,结果显示决策树模型预测较为准确。
结论
通过对放射工作人员心理健康相关因素的分析,提出了将数据挖掘技术应用于职业人群心理健康研究的新思路,扩展了该技术在医学心理健康领域的应用空间。应用数据挖掘技术和现场调查数据库,建立数据挖掘模型,分析放射工作人员心理压力与相关心理健康、社会支持等因素的内在联系,并为心理健康的咨询提供了理论依据和科学指导。
Objective
Excessive perceived stress has a great influence on physical and mental health towards occupational groups in particular in radiological workers.The study of mental health is mainly about single element and there are defects in the conventional analytical methods.The study aims to show the interaction and the quantitative relationship towards psychological stress, life events in the crowd. It's laid the foundation for the improving physical as well as mental well-being, to provide new approach to support in mental health research by data mining.
Subject and methods
The data of this thesis was collected from all the medical radiological workers of the Institute of Occupational Disease Prevention of Henan province in April 2007. Apart from the general demographic surveys, the thesis has also collected psychology questionnaire of the Chinese version Scale Health Scale(CHQ-12)、Eysenck Personality Questionnaire EPQ Chinese Version (EPQ-RSC)、Chinese version of Perceived Stress Scale(CPSS)、Maslach burnout inventory general version (MBI-GS)、Life Event Scale (LES)、Trait Coping Style Questionnaire (TCSQ)、Social Support Rating Scale(SSS).To analyze the data by data mining, at first preprocessed the data to get the effective. The indicators compare with the total score about stress by correlation analysis and multiple regressions analysis. We selected the indicators which had a greater correlation with the stress, established the decision tree model to explore the communication between indicators and the stress.All data analysis realized in SPSS Clementine 11.1.
Results
1.According to analysis for correlation and multiple regressions, study shows that perceived stress effected jointly by emotional exhaustion, passive neglect, psychoticism, positive response and subjective support et al.We selected the indicators and established the decision tree model.
2.We selected the arguments which has statistical significance for perceived stress as dependent variable by decision tree model,and we sorted grade arguments for eight variables, primary for emotional exhaustion, passive neglect, positive response, subjective support, psychoticism, positive events (good) to stimulate, negative response, the amount of professional efficacy.
3.The rules for 11 can also analysed process and result clearly. It is not under perceived stress in (1)the scores of negative events (bad) to stimulate is less then 3, the amount of professional efficacy is less then 22, negative response is less then 25, positive events (good) to stimulate is less then 11,psychoticism is less then 3, subjective support is less then 28, positive coping is less then 34, passive neglect is less then 5,emotional exhaustion is less then 12; (2)the scores of subjective support is more then 28, positive coping is less then 34, passive neglect is less then 5,emotional exhaustion is less then 12; (3)the scores of positive coping is more then 34, passive neglect is less then 5,emotional exhaustion is less then 12.
And it is under perceived stress in (1)the scores of negative response is more then 25, positive events (good) to stimulate is less then 11,psychoticism is less then 3, subjective support is less then 28, positive coping is less then 34, passive neglect is less then 5, emotional exhaustion is less then 12; (2)the scores of psychoticism is less then 3, positive events (good) to stimulate is more then 11,subjective support is less then 28, positive coping is less then 34, passive neglect is less then 5,emotional exhaustion is more then 12; (3)the scores of passive neglect is more then 5,emotional exhaustion is more then 12.
4. The decision tree model predicts the outcome which is better by training and test sets, risk of statistics and consistency test.
Conclusions
We analysed the mental health in radiological workers and proposed the new idea to apply data mining technology to the mental health in radiological workers.It is quite adept at the psychological health of medical by data mining. We established the decision tree model by data mining and database. To analyse the stress and its relationship with their psychological health, social support,and to provide theory and scientific guidelines in radiological workers.
知觉压力对个体身心健康有很大影响,普遍存在于不同职业的人群中,放射工作人群作为一种特定类型的职业群体,也不例外,该人群的心理健康的研究大多因素较单一,传统的分析方法存在缺陷。该研究通过分析多个因素对放射工作人员知觉压力的影响,揭示该人群心理健康、生活事件等因素在压力事件中的交互作用及量化关系,为数据挖掘技术应用于健康心理学的研究提供新思路。
对象与方法
对象来自于2007年4月在河南省职业病防治研究所进行体检的放射工作人员。资料为人口学调查以及心理学方面测量的量表,量表包括一般健康量表(CHQ-12)、艾森克人格简式量表(EPQ-RSC)、知觉压力量表(CPSS)、工作倦怠量表(MBI-GS)、生活事件量表(LES)、特质应对方式量表(TCSQ)、社会支持评定量表(SSS)。利用数据挖掘对资料进行分析,数据预处理,各指标测量分值分别与压力总分进行相关性分析和多重回归分析。选择相关指标,建立决策树模型,并对模型进行评价。数据分析在SPSS Clementinell.1中实现。
结果
1.根据相关性分析和多重回归分析结果显示,情绪耗竭、消极怠慢、精神质、消极应对和社会主观支持等因素对知觉压力有较大影响,选择相关指标,建立决策树模型。
2.决策树模型中将知觉压力作为因变量,筛选有统计意义的自变量,按照等级排序分别为：情绪耗竭、消极怠慢、积极应对、主观支持、精神质、正性生活刺激、消极应对、专业效能感,共筛选8个自变量。
3.模型按照是否产生知觉压力得到了11条分类,部分规则如下：
未产生知觉压力的规则：(1)负性生活事件刺激量≤3,专业效能感≤22,消极应对≤25,正性生活事件刺激量≤11,精神质≤3,主观支持≤28,积极应对≤34,消极怠慢≤5,情绪耗竭≤12；(2)主观支持>28,积极应对≤34,消极怠慢≤5,情绪耗竭≤12；(3)积极应对>34,消极怠慢≤5,情绪耗竭≤12。
产生知觉压力的规则：(1)消极应对>25,正性生活事件刺激量≤11,精神质≤3,主观支持≤28,积极应对≤34,消极怠慢≤5,情绪耗竭≤12；(2)精神质≤3,正性生活事件刺激量>11,主观支持≤28,积极应对≤34,消极怠慢≤5,情绪耗竭≤12；(3)消极怠慢>5,情绪耗竭≤12。
4.利用训练集和测试集拟合和训练模型,RISK统计量和一致性检验测试模型,结果显示决策树模型预测较为准确。
结论
通过对放射工作人员心理健康相关因素的分析,提出了将数据挖掘技术应用于职业人群心理健康研究的新思路,扩展了该技术在医学心理健康领域的应用空间。应用数据挖掘技术和现场调查数据库,建立数据挖掘模型,分析放射工作人员心理压力与相关心理健康、社会支持等因素的内在联系,并为心理健康的咨询提供了理论依据和科学指导。
Objective
Excessive perceived stress has a great influence on physical and mental health towards occupational groups in particular in radiological workers.The study of mental health is mainly about single element and there are defects in the conventional analytical methods.The study aims to show the interaction and the quantitative relationship towards psychological stress, life events in the crowd. It's laid the foundation for the improving physical as well as mental well-being, to provide new approach to support in mental health research by data mining.
Subject and methods
The data of this thesis was collected from all the medical radiological workers of the Institute of Occupational Disease Prevention of Henan province in April 2007. Apart from the general demographic surveys, the thesis has also collected psychology questionnaire of the Chinese version Scale Health Scale(CHQ-12)、Eysenck Personality Questionnaire EPQ Chinese Version (EPQ-RSC)、Chinese version of Perceived Stress Scale(CPSS)、Maslach burnout inventory general version (MBI-GS)、Life Event Scale (LES)、Trait Coping Style Questionnaire (TCSQ)、Social Support Rating Scale(SSS).To analyze the data by data mining, at first preprocessed the data to get the effective. The indicators compare with the total score about stress by correlation analysis and multiple regressions analysis. We selected the indicators which had a greater correlation with the stress, established the decision tree model to explore the communication between indicators and the stress.All data analysis realized in SPSS Clementine 11.1.
Results
1.According to analysis for correlation and multiple regressions, study shows that perceived stress effected jointly by emotional exhaustion, passive neglect, psychoticism, positive response and subjective support et al.We selected the indicators and established the decision tree model.
2.We selected the arguments which has statistical significance for perceived stress as dependent variable by decision tree model,and we sorted grade arguments for eight variables, primary for emotional exhaustion, passive neglect, positive response, subjective support, psychoticism, positive events (good) to stimulate, negative response, the amount of professional efficacy.
3.The rules for 11 can also analysed process and result clearly. It is not under perceived stress in (1)the scores of negative events (bad) to stimulate is less then 3, the amount of professional efficacy is less then 22, negative response is less then 25, positive events (good) to stimulate is less then 11,psychoticism is less then 3, subjective support is less then 28, positive coping is less then 34, passive neglect is less then 5,emotional exhaustion is less then 12; (2)the scores of subjective support is more then 28, positive coping is less then 34, passive neglect is less then 5,emotional exhaustion is less then 12; (3)the scores of positive coping is more then 34, passive neglect is less then 5,emotional exhaustion is less then 12.
And it is under perceived stress in (1)the scores of negative response is more then 25, positive events (good) to stimulate is less then 11,psychoticism is less then 3, subjective support is less then 28, positive coping is less then 34, passive neglect is less then 5, emotional exhaustion is less then 12; (2)the scores of psychoticism is less then 3, positive events (good) to stimulate is more then 11,subjective support is less then 28, positive coping is less then 34, passive neglect is less then 5,emotional exhaustion is more then 12; (3)the scores of passive neglect is more then 5,emotional exhaustion is more then 12.
4. The decision tree model predicts the outcome which is better by training and test sets, risk of statistics and consistency test.
Conclusions
We analysed the mental health in radiological workers and proposed the new idea to apply data mining technology to the mental health in radiological workers.It is quite adept at the psychological health of medical by data mining. We established the decision tree model by data mining and database. To analyse the stress and its relationship with their psychological health, social support,and to provide theory and scientific guidelines in radiological workers.
引文
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[51]Radwan E,Tazaki E. Rough sets and genetic algorithms in learning cellular neural networks cloning template for decision making system[J]. Int J Neural Syst,2004,14 (1):57～68.
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