In this thesis several studies are presented that have targeted decision making about case management plans in probation. In a case management plan probation officers describe the goals and interventions that should help offenders stop reoffending, and the specific measures necessary to reduce acute risks of recidivism and harm. Such a plan is embedded in a judicial framework, a sanction or advice about the sanction in which these interventions and measures should be executed. The topic of this thesis is the use of structured decision support, and the question is if this can improve decision making about case management plans in probation and subsequently improve the effectiveness of offender supervision. In this chapter we first sketch why structured decision making was introduced in the Dutch probation services. Next we describe the instrument for risk and needs assessment as well as the procedure to develop case management plans that are used by the Dutch probation services and that are investigated in this thesis. Then we describe the setting of the studies and the research questions, and we conclude with an overview of this thesis.
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Peer-reviewed artikel over semantische segmentatie van point clouds.
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Abstract: Technological innovation in the healthcare sector is increasing, but integration of information technology (IT) in the care process is difficult. Healthcare workers are important agents in this IT integration. The purpose of this study is to explore factors that feed motivation to use IT. Self-determination theory (SDT) is applied to study how motivational factors impact effective IT use among frontline caregivers in residential care settings. As the team is very important to these caregivers, the team is our unit of analysis. In an embedded single case study design, interviews were conducted with all nine members of a team effectively using IT. All three basic psychological needs from SDT - autonomy, competence and relatedness - were found to have impact on effective IT use, though autonomy was primarily experienced at team level. Conversely, the effective use of an IT collaboration tool influences relatedness.
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AbstractPurpose: Purpose: To investigate the ability of case managers, working in ambulatory treatment settings specialized in addiction care, to clinically judge demoralization insubstancedependent patients. Design and Methods: In a crosssectional study, clinical judgments of case managerswere compared with the patients' scores on the Demoralization Scale, by calculatingthe sensitivity and specificity scores. Findings: Case managers identified demoralization in 85% of the cases (sensitivity),the specificity of 62% suggests that demoralization was overestimated by casemanagers. Practice Implications: Demoralization is a frequently occurring phenomenon inpatients. Methods should be developed that allow professionals and patients toidentify demoralization collaboratively, and to develop tailored interventions toprevent demoralization and its negative consequences
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Individuals with mild intellectual disabilities or borderline intellectual functioning are at increased risk to develop a substance use disorder—however, effective treatment programs adapted to this target group are scarce. This study evaluated the effectiveness of Take it Personal!+ in individuals with mild intellectual disabilities or borderline intellectual functioning and substance use disorder. Take it Personal!+ is a personalized treatment based on motivational interviewing and cognitive-behavioral therapy supported by an mHealth application. Data were collected in a nonconcurrent multiple baseline single-case experimental design across individuals with four phases (i.e., baseline, treatment, posttreatment, and follow-up). Twelve participants were randomly allocated to baseline lengths varying between 7 and 11 days. Substance use quantity was assessed during baseline, treatment, and posttreatment with a daily survey using a mobile application. Visual analysis was supported with statistical analysis of the daily surveys by calculating three effect size measures in 10 participants (two participants were excluded from this analysis due to a compliance rate below 50%). Secondary, substance use severity was assessed with standardized questionnaires at baseline, posttreatment, and follow-up and analyzed by calculating the Reliable Change Index. Based on visual analysis of the daily surveys, 10 out of 12 participants showed a decrease in mean substance use quantity from baseline to treatment and, if posttreatment data were available, to posttreatment. Statistical analysis showed an effect of Take it Personal!+ in terms of a decrease in daily substance use in 8 of 10 participants from baseline to treatment and if posttreatment data were available, also to posttreatment. In addition, data of the standardized questionnaires showed a decrease in substance use severity in 8 of 12 participants. These results support the effectiveness of Take it Personal!+ in decreasing substance use in individuals with mild intellectual disabilities or borderline intellectual functioning.
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Car use in the sprawled urban region of Noord‐Brabant is above the Dutch average. Does this reflect car dependency due to the lack of competitive alternative modes? Or are there other factors at play, such as differences in preferences? This article aims to determine the nature of car use in the region and explore to what extent this reflects car dependency. The data, comprising 3,244 respondents was derived from two online questionnaires among employees from the High‐Tech Campus (2018) and the TU/e‐campus (2019) in Eindhoven. Travel times to work by car, public transport, cycling, and walking were calculated based on the respondents’ residential location. Indicators for car dependency were developed using thresholds for maximum commuting times by bicycle and maximum travel time ratios between public transport and car. Based on these thresholds, approximately 40% of the respondents were categorised as car‐dependent. Of the non‐car‐dependent respondents, 31% use the car for commuting. A binomial logit model revealed that higher residential densities and closer proximity to a railway station reduce the odds of car commuting. Travel time ratios also have a significant influence on the expected directions. Mode choice preferences (e.g., comfort, flexibility, etc.) also have a significant, and strong, impact. These results highlight the importance of combining hard (e.g., improvements in infrastructure or public transport provi-sion) and soft (information and persuasion) measures to reduce car use and car dependency in commuting trips.
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Abstract Aim Evaluate the impact of LS@H project participation on stakeholders. Background As populations age and workforces decline, care technology in nursing is becoming increasingly commonplace. Collaboration between nurse academia, education and practice can result in practice-based research and meaningful learning for nursing students and staff. However, little is known about the factors influencing effective collaboration. Based on the knowledge that narratives can be an effective vehicle for healthcare practice change, a Dutch school of nursing and its practice partners collaborated on the Living Longer and Safe at Home! (LS@H) project. This project aimed to explore a more person-centred approach to the use of technology in nursing care. Having gathered data from multiple sources to construct case narratives on the use of technology in older persons care, students nurses were able to contribute to practice development as their narratives were fed back to local and regional teams. Design To evaluate the impact of the LS@H project, we employed the same methodology used in the project: mixed data gathering methods to construct a case narrative. LS@H project students, supervisors, mentors and higher management shared their experiences and the research team constructed the case narrative. Methods Qualitative data were gathered via individual, duo and group interviews and supplemented with a survey among students. Transcription and thematic analysis followed, with multiple rounds of critical peer review before the thematic framework was agreed, survey results integrated and the case narrative constructed. Results According to stakeholder participants, the LS@H project led to an unfreezing of the status quo in both education and practice. The approach was new and guidelines with community support was needed to allay fears. The project design enabled a sense of shared ownership, across individuals and organisations for improving practice. Perspectives on the use of technology and older persons nursing were transformed. Critically dialoguing case narratives encouraged purposeful action to improve practice and fostered reflective practice among students and teams. Conclusions Collaborative practice-based research can be a valuable learning experience for student nurses, positively influencing their view of nursing practice as well as enabling them to actively contribute to practice development. Adequate preparation, supervision and practice mentorship is vital, alongside practice (leader) commitment to ensure continued student assignments with subsequent critical dialogue of the multi-stakeholder case narratives produced.
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aving access to accurate and recent digital twins of infrastructure assets benefits the renovation, maintenance, condition monitoring, and construction planning of infrastructural projects. There are many cases where such a digital twin does not yet exist, such as for legacy structures. In order to create such a digital twin, a mobile laser scanner can be used to capture the geometric representation of the structure. With the aid of semantic segmentation, the scene can be decomposed into different object classes. This decomposition can then be used to retrieve cad models from a cad library to create an accurate digital twin. This study explores three deep-learning-based models for semantic segmentation of point clouds in a practical real-world setting: PointNet++, SuperPoint Graph, and Point Transformer. This study focuses on the use case of catenary arches of the Dutch railway system in collaboration with Strukton Rail, a major contractor for rail projects. A challenging, varied, high-resolution, and annotated dataset for evaluating point cloud segmentation models in railway settings is presented. The dataset contains 14 individually labelled classes and is the first of its kind to be made publicly available. A modified PointNet++ model achieved the best mean class Intersection over Union (IoU) of 71% for the semantic segmentation task on this new, diverse, and challenging dataset.
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