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The Student Well-Being Process Questionnaire (Student WPQ)

机译:学生幸福的过程问卷(学生WPQ)

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Recent research has used short questionnaires based on single item versions of psychosocial concepts to assess well-being. This has largely been confined to occupational samples and the present article describes the extension of this process to university students. The Student Well-being Process Questionnaire (Student WPQ) was used to examine predictors of positive well-being, negative mental health and cognitive function. An online survey was used with 478 first and second year undergraduates as participants. Regression analyses showed that positive well-being (e.g. happiness, positive affect and life satisfaction) was predicted by positive personality (high optimism, self-esteem and self-efficacy), high social support and low stressors and low negative coping scores. Negative outcomes (e.g. perceived stress, anxiety and depression) were predicted by high stressor, coping and conscientiousness scores, and low positive personality and social support scores. Cognitive problems were predicted by high stressor and negative coping scores and low positive personality scores. A MANOVA showed that there were no significant interactions between the predictor variables. The best predictor of all outcomes was a combined score including all predictor variables. Overall, the present study shows that the Student WPQ can provide useful information on predictors of different aspects of well-being. Future research can include additional potential predictors and other outcomes to determine whether other factors are significant when established predictors are adjusted for.
机译:最近的研究使用了基于单项版本的心理社会概念的短暂问卷来评估福祉。这主要被限制在职业样本中,本文介绍了这一过程的延伸到大学生。学生幸福的过程调查问卷(学生WPQ)用于检查积极福祉,负面心理健康和认知功能的预测因子。在线调查与参与者为478名和第二年的本科生使用。回归分析表明,积极的幸福(例如幸福,积极的影响和生活满意度)预测为积极的个性(高乐观,自尊,自我效能,高社会支持和低压力源以及低负面应激成绩。通过高压力源,应对和剥夺性评分,以及低积极的人格和社会支持分数,预测了负面结果(例如感知压力,焦虑和抑郁)。通过高压力源和负面应对分数和低积极人格评分预测认知问题。 Manova表明预测变量之间没有显着的相互作用。所有结果的最佳预测因子是一个组合得分,包括所有预测因子变量。总体而言,本研究表明,学生WPQ可以提供有关福祉不同方面的预测因子的有用信息。未来的研究可以包括额外的潜在预测因子和其他结果,以确定在确定建立的预测器时是否存在其他因素。

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