The Short-Term Assessment of Risk and Treatability: Adolescent Version (START:AV) is a risk assessment instrument for adolescents that estimates the risk of multiple adverse outcomes. Prior research into its predictive validity is limited to a handful of studies conducted with the START:AV pilot version and often by the instrument’s developers. The present study examines the START:AV’s field validity in a secure youth care sample in the Netherlands. Using a prospective design, we investigated whether the total scores, lifetime history, and the final risk judgments of 106 START:AVs predicted inpatient incidents during a 4-month follow-up. Final risk judgments and lifetime history predicted multiple adverse outcomes, including physical aggression, institutional violations, substance use, self-injury, and victimization. The predictive validity of the total scores was significant only for physical aggression and institutional violations. Hence, the short-term predictive validity of the START:AV for inpatient incidents in a residential youth care setting was partially demonstrated and the START:AV final risk judgments can be used to guide treatment planning and decision-making regarding furlough or discharge in this setting.
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Current research on data in policy has primarily focused on street-level bureaucrats, neglecting the changes in the work of policy advisors. This research fills this gap by presenting an explorative theoretical understanding of the integration of data, local knowledge and professional expertise in the work of policy advisors. The theoretical perspective we develop builds upon Vickers’s (1995, The Art of Judgment: A Study of Policy Making, Centenary Edition, SAGE) judgments in policymaking. Empirically, we present a case study of a Dutch law enforcement network for preventing and reducing organized crime. Based on interviews, observations, and documents collected in a 13-month ethnographic fieldwork period, we study how policy advisors within this network make their judgments. In contrast with the idea of data as a rationalizing force, our study reveals that how data sources are selected and analyzed for judgments is very much shaped by the existing local and expert knowledge of policy advisors. The weight given to data is highly situational: we found that policy advisors welcome data in scoping the policy issue, but for judgments more closely connected to actual policy interventions, data are given limited value.
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Using path analysis, the present study focuses on the development of a model describing the impact of four judgments of self-perceived academic competence on higher education students' achievement goals, learning approach, and academic performance. Results demonstrate that academic self-efficacy, self-efficacy for self-regulated learning, academic self-concept, and perceived level of understanding are conceptually and empirically distinct self-appraisals of academic competence which have a different impact on student motivation, learning, and academic performance. Furthermore, the current study suggests that students reflecting high scores on the four measures of self-perceived competence, are more persistent, more likely to adopt mastery and/or performance approach goals, less anxious, process the learning material at a deeper level, and achieve better study results. However, this study also warns that high self-perceived competence (e.g., perceived level of understanding), if not accompanied by a mastery goal orientation, can turn into overconfidence resulting in lower persistence levels and poorer study results.
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