New assessment systems for residential treatment,management, research,and evaluation: A symposium

An introduction and overview are presented to new observational assessment systems for ongoing assessment and monitoring of both staff and resident (client or patient) functioning in residential treatment programs for emotionally disturbed and mentally retarded adults.Preparation of articles and the research and development on which the articles are based was supported, in part, by Public Health Service Grants MH-15553 and MH-25464 from the National Institute of Mental Health, and by grants from the Joyce Foundation and the Illinois Department of Mental Health and Developmental Disabilities.A symposium presented at the 87th Annual Meetings of the American Psychological Association, New York City, September 1979.     

Systems use to objectify program evaluation,clinical, and management decisions

The utility of the observational assessment systems at different levels — from local clinical to systemwide management — is outlined. An overview is provided of TSBC information applied to individualized problem identification and programming/monitoring, discharge and competency determinations, and both absolute and comparative program evaluation. The utility of SRIC information for prgoramming/monitoring and for staff training and evaluation is outlined in addition to the applied uses of the information of the assessment systems in combination. Once implemented, the continuous data from the systems allow for empirically based self-corrective improvements in the quality of mental health services while automatically providing a basis for legal documentation and accurate cost/effectiveness comparisons of mental health programs.Preparation of this article and the research and development on which the article is based were supported, in part, by Public Health Service Grants MH-15553 and MH-25464 from the National Institute of Mental Health, and by grants from The Joyce Foundation and the Illinois Department of Mental Health and Developmental Disabilities.Presented at the 87th Annual Meetings of the American Psychological Association, New York City, September 1979, as part of a symposium on New assessment systems for residential treatment, management, research, and evaluation.     

Observer orientation and actor-observer differences in attributions for failure

In a series of two experiments, male undergraduates either operated or watched another person operate a model racecar set. Instructions varied observers' expectancies about future involvement with the task. The drivers' causal attributions for their performance were compared with those of neutral observers as well as those of involved observers who anticipated running on the track. Predictions, derived from the Jones and Nisbett framework of actor/observer differences, were that actors should make more external than internal attributions and that neutral or “passive” observers should do the opposite. Moreover, the involved or “active” observer groups were expected to display attributional patterns similar to those of the actors. These predictions were confirmed. Results are discussed in terms of information and information-processing differences between groups.     

Prospective and Concurrent Affective Dynamics in Self-Injurious Thoughts and Behaviors: An Examination in Young Adult Women

Affective dynamics, assessed using ecological momentary assessment (EMA), provide a nuanced understanding of within-person fluctuations of negative affect (NA) and positive affect (PA) in daily life. NA and PA dynamics have been associated with psychopathology and response to psychological treatments. NA and PA dynamics have been rarely studied concurrently in association with self-injurious thoughts and behaviors (SITB), transdiagnostic difficulties encountered regularly in clinical and community settings. Here we present EMA data from a large, diverse sample of young adult women with high rates of SITB to examine NA and PA dynamics (mean intensity, variability, and inertia). Specifically, we considered the prospective associations between past-year suicidal thoughts and past-year nonsuicidal self-injury and affective dynamics, as well as the concurrent associations between affective dynamics, EMA-reported suicidal thoughts, and EMA-reported urges for nonsuicidal self-injury. Results demonstrate that elevated mean NA and NA variability are robustly associated with all types of SITB assessed prospectively or concurrently. Interestingly, these associations were weakest for past-year nonsuicidal self-injurious behaviors, relative to past-year and concurrent suicidal or nonsuicidal self-injurious thoughts. Past-year suicidal thoughts further predicted increased NA inertia. Decreased PA inertia was associated with past-year nonsuicidal self-injury behavior, as well as concurrent EMA suicidal thoughts. We found no associations (prospective or concurrent) between SITB and mean PA intensity or PA variability. These results highlight the importance of understanding affective processes to develop real-world interventions to prevent nonsuicidal self-injury and suicidal behavior in daily life.     

Quitters would not prosper: Examining the relationship between online assessment performance and assessment attrition behavior

Hiring managers are often hesitant to implement online assessments over concerns that higher‐quality candidates are more likely to quit lengthy assessment batteries than lower‐quality candidates. In this paper, we present the results of two studies that collectively challenge this assumption. In Study 1, data from 327,517 job‐seekers spanning eight hiring contexts showed that assessment performance was negatively associated with subsequent assessment attrition behavior. In Study 2, we replicated this pattern of effects in two additional samples comprising 1,844,604 applicants for hourly associate positions, and 18,937 applicants for nursing positions. Collectively, these findings suggest that some degree of attrition may ultimately benefit, rather than harm, organizations using prehire assessments.     

Support for distracted driving laws: An analysis of adolescent drivers from the Traffic Safety Culture Index from 2011 to 2017

IntroductionAdolescent drivers are often the focus of traffic safety legislation as they are at increased risk for crash-related injury and death. However, the degree to which adolescents support distracted driving laws and factors contributing to their support are relatively unknown. Using a large, nationally weighted sample of adolescent drivers in the United States, we assessed if perceived threat from other road users’ engagement in distracted driving, personal engagement in distracted driving behaviors, and the presence of state distracted driving laws was associated with support for distracted driving laws.MethodsThe sample included 3565 adolescents (aged 16–18) who participated in the Traffic Safety Culture Index survey from 2011 to 2017. A modified Poisson regression model with robust errors was fit to the weighted data to examine support for distracted driving laws. Models included age, gender, year, state distracted driving laws, personal engagement in distracted driving behavior, and perceived threat from other road users’ engaging in distracted driving.ResultsApproximately 87% of adolescents supported a law against texting and emailing compared to 66% who supported a universal handheld cellphone law. Support for distracted driving legislation was associated with greater perceived threat of other road users engaging in distracted driving while accounting for personal engagement in distracted driving, state distracted driving laws, and developmental covariates.DiscussionGreater understanding of the factors behind legislative support is needed. Public health interventions focused on effectively translating the risks of cellphone use while driving and effective policy will further improve the traffic safety culture.     

情感计算技术如何推动犯罪风险评估工具的发展？

摘 要 随着近年来人工智能深度学习技术的发展,情感计算与人格计算技术日渐成熟,在许多实际应用场景中取得了良好的表现,当前人工智能情感计算技术应用于犯罪风险评估领域,能够有效解决目前主流的风险评估工具难以解决的个体内差异性的预测因子评估问题以及被测评参与者因社会赞许性而导致结果失真的问题。本文在详细阐述目前主流评估工具的局限性基础上,详细阐述了以情感计算技术为支撑的动态风险评估工具的设计思路、目前已有的技术方案以及设计细节的理论依据,在此基础上最后提出以人工智能技术为支撑的新型评估工具的未来发展方向。     

多级评分认知诊断模型述评

认知诊断评估旨在探讨个体内部的知识掌握结构,并提供关于学生优缺点的详细诊断信息,以促进个体的全面发展。当前研究者已开发了大量0-1评分的认知诊断模型,但对于多级评分认知诊断模型的研究还比较少。本文对已有的多级评分认知诊断模型进行了归纳,介绍了模型的假设,计量特征以及适用范围,为实际应用者和研究者在多级评分认知诊断模型的比较和选用上提供借鉴和参考。最后,对未来关于多级评分诊断模型的研究方向进行了展望。     

心理因素与学业表现：机器学习分类预测模型

随着高等教育规模的扩大,学业表现不良逐渐成为一个不容忽视的现象,对学业表现不良的学生进行预测并提早给予干预可降低退学率并减少教育资源的损失。由于导致学业表现不良的因素众多且关系复杂,传统的基于相关分析的研究方法很难建立早期预测模型并进行应用。本研究旨在利用机器学习算法,对数据进行挖掘,并建立学业表现预测模型。研究对653名大一新生的心理健康状况、应对方式、人格、内外控倾向和相关人口统计学信息进行了收集,并在一年后采集了其学业成绩,利用随机森林(RF)、K邻近（KNN）、支持向量机（SVM）、决策树（DT）、朴素贝叶斯（NB）等机器学习算法建立了分类模型。结果显示,随机森林算法在识别学业表现不良学生时有最好的表现,其中准确率95.86%, 召回率91.83%,f1分数为93.80%。特征重要性分析显示,前10个对模型有最高贡献度的特征包括：年龄、性别、是否为独生子、内外控倾向、神经质倾向、积极应对倾向、宜人性倾向、一般症状指数、开放性倾向和焦虑水平。为避免过度拟合问题,本研究在一年后收集的166名新生样本中进行了模型验证,结果显示模型在新数据集上有较好的泛化表现,其中f1分数90.90％,准确率92.60％,召回率89.26％。研究提示基于人口统计学和心理测评信息,机器学习算法有助于及早识别学业表现不良学生并为开展早期干预提供启示。     

Improved Test–retest Reliability Criteria as Applied to Probability Assessments

Test–retest reliability is a common indicator of response consistency. It is argued that using regression coefficients for detecting systematic response error is less appropriate than testing for shifts in the mean (median) and variance. This procedure is exemplified using probability response data. For this data, shifts in centrality were found to be about 2.5 times more likely than shifts in variability. The shifts in centrality did not favor any particular direction; however, variability tended to decrease over time in early sessions.