PURPOSE: To investigate factors that influence participation in and needs for work and other daytime activities among individuals with severe mental illnesses (SMI). METHODS: A latent class analysis using routine outcome monitoring data from 1069 patients was conducted to investigate whether subgroups of individuals with SMI can be distinguished based on participation in work or other daytime activities, needs for care in these areas, and the differences between these subgroups. RESULTS: Four subgroups could be distinguished: (1) an inactive group without daytime activities or paid employment and many needs for care in these areas; (2) a moderately active group with some daytime activities, no paid employment, and few needs for care; (3) an active group with more daytime activities, no paid employment, and mainly met needs for care; and (4) a group engaged in paid employment without needs for care in this area. Groups differed significantly from each other in age, duration in MHC, living situation, educational level, having a life partner or not, needs for care regarding social contacts, quality of life, psychosocial functioning, and psychiatric symptoms. Differences were not found for clinical diagnosis or gender. CONCLUSIONS: Among individuals with SMI, different subgroups can be distinguished based on employment situation, daytime activities, and needs for care in these areas. Subgroups differ from each other on patient characteristics and each subgroup poses specific challenges, underlining the need for tailored rehabilitation interventions. Special attention is needed for individuals who are involuntarily inactive, with severe psychiatric symptoms and problems in psychosocial functioning.
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We present a novel hierarchical model for human activity recognition. In contrast with approaches that successively recognize actions and activities, our approach jointly models actions and activities in a unified framework, and their labels are simultaneously predicted. The model is embedded with a latent layer that is able to capture a richer class of contextual information in both state-state and observation-state pairs. Although loops are present in the model, the model has an overall linear-chain structure, where the exact inference is tractable. Therefore, the model is very efficient in both inference and learning. The parameters of the graphical model are learned with a structured support vector machine. A data-driven approach is used to initialize the latent variables; therefore, no manual labeling for the latent states is required. The experimental results from using two benchmark datasets show that our model outperforms the state-of-the-art approach, and our model is computationally more efficient.
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Despite major implications for forensic casework, limited research has been done on investigating secondary transfer of latent fingermarks. Adhesive tapes, such as duct tape, can potentially lift latent fingermarks from other surfaces due to their adhesive properties. This study aimed to investigate the possible secondary transfer between layers of adhesive tape and tape and other substrates (metal and plastic). Fingermarks were directly placed onto a primary substrate and subsequently brought into contact with a secondary substrate for varying duration. After visualization, the quality of the fingermarks was assessed to measure their loss and transfer. It was shown that fresh latent fingermarks can transfer between layers of adhesive tape, with instances of sufficient quality for comparison of the transferred fingermarks. In contrast, no transfer was detected after one week. However, a substantial loss of quality of the initially deposited fingermark was observed, suggesting an influence of time. Overall, it was shown that secondary transfer is possible and that caution has to be taken when analysing and interpreting latent fingermarks on adhesive tapes.
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Travel-related attitudes and dissonance between attitudes and the characteristics of the residential built environment are believed to play an important role in the effectiveness of land use policies that aim to influence travel behaviour. To date, research on the nature and directions of causality of the links between these variables has been hindered by the lack of longitudinal approaches. This paper takes such an approach by exploring how people across different population groups adjust their residential environments and attitudes over time. Two latent class transition models are used to segment a population into consonant and dissonant classes to reveal differences in their adjustment process. Interactions between (1) the distance to railway stations and travel-mode-related attitudes and (2) the distance to shopping centres and the importance of satisfaction with these distances are modelled. The models reveal mixed patterns in consonant and dissonant classes at different distances from these destinations. These patterns remain relatively stable over time. People in more dissonant classes generally do not have a higher probability of switching to more consonant classes. People adjust their built environments as well as their attitudes over time and these processes differ between classes. Implications for policies are discussed.
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Most multi‑problem young adults (18–27 years old) have been exposed to childhood maltreatment and/or have been involved in juvenile delinquency and, therefore, could have had Child Protection Service (CPS) interference during childhood. The extent to which their childhood problems persist and evolve into young adult‑ hood may differ substantially among cases. This might indicate heterogeneous profiles of CPS risk factors. These pro‑ files may identify combinations of closely interrelated childhood problems which may warrant specific approaches for problem recognition and intervention in clinical practice. The aim of this study was to retrospectively identify distinct statistical classes based on CPS data of multi‑problem young adults in The Netherlands and to explore whether these classes were related to current psychological dysfunctioning and delinquent behaviour. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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Measuring values in sociological research sometimes involves the use of ranking data. A disadvantage of a ranking assignment is that the order in which the items are presented might influence the choice preferences of respondents regardless of the content being measured. The standard procedure to rule out such effects is to randomize the order of items across respondents. However, implementing this design may be impractical and the biasing impact of a response order effect cannot be evaluated. We use a latent choice factor (LCF) model that allows statistically controlling for response order effects. Furthermore, the model adequately deals with the known issue of ipsativity of ranking data. Applying this model to a Dutch survey on work values, we show that a primacy effect accounts for response order bias in item preferences. Our findings demonstrate the usefulness of the LCF model in modeling ranking data while taking into account particular response biases.
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Hoofdstuk 3 uit: Coppoolse, R., Van Eijl, P.J. & Pilot, A. (Red.) Hoogvliegers. Ontwikkeling naar professionele excellentie (pp 35-55). Rotterdam: University Press, Hogeschool van Rotterdam. (2013)
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This study used a trait-state-occasion (TSO) model to isolate stable trait variance, occasion-specific state variance, and shared method related variance in a measure for leisure satisfaction in a Dutch nationally representative nine-year panel study. Findings indicate that satisfaction with leisure time is a consistently stronger indicator of overall leisure satisfaction than satisfaction with leisure activities. About half of the variance in leisure satisfaction is stable trait variance, with the remaining variance being mostly occasion-specific and to a lesser extent attributable to shared method variance and error. However, these findings depend on the age group we consider.Several socio-demographic variables relate directly to the trait aspect of leisure satisfaction. Our study underscores the importance of recognizing that over time leisure satisfaction measurements have considerable stable and more volatile elements and that one should control for shared method effects.
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Poster Presentation at the conference of the International Association of Identification (IAI) International Forensic Educational Conference
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