Active learning has become an increasingly popular method for screening large amounts of data in systematic reviews and meta-analyses. The active learning process continually improves its predictions on the remaining unlabeled records, with the goal of identifying all relevant records as early as possible. However, determining the optimal point at which to stop the active learning process is a challenge. The cost of additional labeling of records by the reviewer must be balanced against the cost of erroneous exclusions. This paper introduces the SAFE procedure, a practical and conservative set of stopping heuristics that offers a clear guideline for determining when to end the active learning process in screening software like ASReview. The eclectic mix of stopping heuristics helps to minimize the risk of missing relevant papers in the screening process. The proposed stopping heuristic balances the costs of continued screening with the risk of missing relevant records, providing a practical solution for reviewers to make informed decisions on when to stop screening. Although active learning can significantly enhance the quality and efficiency of screening, this method may be more applicable to certain types of datasets and problems. Ultimately, the decision to stop the active learning process depends on careful consideration of the trade-off between the costs of additional record labeling against the potential errors of the current model for the specific dataset and context.
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Active learning has become an increasingly popular method for screening large amounts of data in systematic reviews and meta-analyses. The active learning process continually improves its predictions on the remaining unlabeled records, with the goal of identifying all relevant records as early as possible. However, determining the optimal point at which to stop the active learning process is a challenge. The cost of additional labeling of records by the reviewer must be balanced against the cost of erroneous exclusions. This paper introduces the SAFE procedure, a practical and conservative set of stopping heuristics that offers a clear guideline for determining when to end the active learning process in screening software like ASReview. The eclectic mix of stopping heuristics helps to minimize the risk of missing relevant papers in the screening process. The proposed stopping heuristic balances the costs of continued screening with the risk of missing relevant records, providing a practical solution for reviewers to make informed decisions on when to stop screening. Although active learning can significantly enhance the quality and efficiency of screening, this method may be more applicable to certain types of datasets and problems. Ultimately, the decision to stop the active learning process depends on careful consideration of the trade-off between the costs of additional record labeling against the potential errors of the current model for the specific dataset and context.
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From the onset of the Corona crisis, a specific policy challenge was identified in the Netherlands: How to motivate young people to adhere to the behavioral measures, such as physical distancing? Young people have an important role to play in stopping the virus from spreading, but they may be more difficult to reach and less motivated and able to adhere to the guidelines than adults. Mid-March, Moniek Buijzen was invited to consult the behavioral unit of the Dutch national health institute (RIVM) on communication and behavioral change among youth. She immediately called together the Dutch Young Consumer Network, which consists of scholars with expertise in communication directed at children and adolescents. Over the months, our network has been approached by policymakers, campaign developers, and journalists and engaged in a wide variety of advice activities. Even though the crisis is not over yet, we would like to share the collaborative approach that we took to harness our expertise and, most importantly, the specific tool that we used to share it.
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Het doel van dit onderzoek is te onderzoeken onder welke omstandigheden en onder welke condities relatief moderne modelleringstechnieken zoals support vector machines, neural networks en random forests voordelen zouden kunnen hebben in medisch-wetenschappelijk onderzoek en in de medische praktijk in vergelijking met meer traditionele modelleringstechnieken, zoals lineaire regressie, logistische regressie en Cox regressie.
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The aim of this study was to assess the association between prescription changes frequency (PCF) and hospital admissions and to compare the PCF to the Chronic Disease Score (CDS). The CDS measures comorbidity on the basis of the 1-year pharmacy dispensing data. In contrast, the PCF is based on prescriptionchanges over a 3-month period. A retrospective matched case–control design was conducted. 10.000 patients were selected randomly from the Dutch PHARMO database, who had been hospitalized (index date) between July 1, 1998 and June 30, 2000. The primary study outcome was the number of prescription changes during several three-month time periods starting 18, 12, 9, 6, and 3 months before the index date. For each hospitalized patient, one nonhospitalized patient was matched for age, sex, and geographic area, and was assigned the same index date as the corresponding hospitalized patient.We classified four mutually exclusive types of prescription changes: change in dosage, switch, stop and start.
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Background. Adequate and user-friendly instruments for assessing physical function and disability in older adults are vital for estimating and predicting health care needs in clinical practice. The Late-Life Function and Disability Instrument Computer Adaptive Test (LLFDICAT) is a promising instrument for assessing physical function and disability in gerontology research and clinical practice. Objective. The aims of this study were: (1) to translate the LLFDI-CAT to the Dutch language and (2) to investigate its validity and reliability in a sample of older adults who spoke Dutch and dwelled in the community. Design. For the assessment of validity of the LLFDI-CAT, a cross-sectional design was used. To assess reliability, measurement of the LLFDI-CAT was repeated in the same sample. Methods. The item bank of the LLFDI-CAT was translated with a forward-backward procedure. A sample of 54 older adults completed the LLFDI-CAT, World Health Organization Disability Assessment Schedule 2.0, RAND 36-Item Short-Form Health Survey physical functioning scale (10 items), and 10-Meter Walk Test. The LLFDI-CAT was repeated in 2 to 8 days (mean4.5 days). Pearson’s r and the intraclass correlation coefficient (ICC) (2,1) were calculated to assess validity, group-level reliability, and participant-level reliability. Results. A correlation of .74 for the LLFDI-CAT function scale and the RAND 36-Item Short-Form Health Survey physical functioning scale (10 items) was found. The correlations of the LLFDI-CAT disability scale with the World Health Organization Disability Assessment Schedule 2.0 and the 10-Meter Walk Test were .57 and .53, respectively. The ICC (2,1) of the LLFDI-CAT function scale was .84, with a group-level reliability score of .85. The ICC (2,1) of the LLFDI-CAT disability scale was .76, with a group-level reliability score of .81. Limitations. The high percentage of women in the study and the exclusion of older adults with recent joint replacement or hospitalization limit the generalizability of the results. Conclusions. The Dutch LLFDI-CAT showed strong validity and high reliability when used to assess physical function and disability in older adults dwelling in the community.
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Online support communities are gaining attention among child-attracted persons (CAPs). Though research has largely focused on the negative consequences these environments create for potential offending, they may also provide a beneficial alternative to more formal treatment settings. To assess the utility for clinical and therapeutic purposes, this analysis focused on subcultural dynamics to examine self-reported wellbeing outcomes of participation in a Dutch forum for CAPs. A total of 15 semi-structured interviews were conducted with moderators, members and mental health professionals involved in the community. Thematic analyses demonstrated that by means of informal social control, bonds of trust and social relational education, the network aims to regulate the behavior and enhance the wellbeing of its marginalized participants. Key outcomes include a decreased sense of loneliness and better coping with stigma, to the point that participants experience less suicidal thoughts. Association with prosocial peers also helps to set moral boundaries regarding behavior towards children, although we cannot fully rule out potential adverse influences. Online support networks offer a stepping stone to professional care that fits individual needs of CAPs, while also providing an informal environment that overcomes limitations of physical therapy and that extents principles of existing prevention and desistance approaches. Gepubliceerd door uitgever Sage: Bekkers, L. M. J., Leukfeldt, E. R., & Holt, T. J. (2024). Online Communities for Child-Attracted Persons as Informal Mental Health Care: Exploring Self-Reported Wellbeing Outcomes. Sexual Abuse, 36(2), 158-184. https://doi.org/10.1177/10790632231154882
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Background: Follow-up of stroke survivors is important to objectify activity limitations and/or participations restrictions. Responsive measurement tools are needed with a low burden for professional and patient. Aim: To examine the concurrent validity, floor and ceiling effects and responsiveness of both domains of the Late-Life Function and Disability Index Computerized Adaptive Test (LLFDI-CAT) in first-ever stroke survivors discharged to their home setting. Design: Longitudinal study. Setting: Community. Population: First ever stroke survivors. Methods: Participants were visited within three weeks after discharge and six months later. Stroke Impact Scale (SIS 3.0) and Five-Meter Walk Test (5MWT) outcomes were used to investigate concurrent validity of both domains, activity limitations, and participation restriction, of the LLFDI-CAT. Scores at three weeks and six months were used to examine floor and ceiling effects and change scores were used for responsiveness. Responsiveness was assessed using predefined hypotheses. Hypotheses regarding the correlations with change scores of related measures, unrelated measures, and differences between groups were formulated. Results: The study included 105 participants. Concurrent validity (R) of the LLFDI-CAT activity limitations domain compared with the physical function domain of the SIS 3.0 and with the 5MWT was 0.79 and -0.46 respectively. R of the LLFDI-CAT participation restriction domain compared with the participation domain of the SIS 3.0 and with the 5MWT was 0.79 and -0.41 respectively. A ceiling effect (15%) for the participation restriction domain was found at six months. Both domains, activity limitations and participation restrictions, of the LLFDI-CAT, scored well on responsiveness: 100% (12/12) and 91% (12/11) respectively of the predefined hypotheses were confirmed. Conclusions: The LLFDI-CAT seems to be a valid instrument and both domains are able to detect change over time. Therefore, the LLFDI-CAT is a promising tool to use both in practice and in research. Clinical rehabilitation impact: The LLFDI-CAT can be used in research and clinical practice.
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This paper describes prototypes for transition pathways towards inclusive, sustainable development for seven regions in five European Countries. The approach for developing transition pathways was based on three theoretical building blocks. First, the ABCD-Roadmap that outlines the various steps to be developed in the design process of the transition pathway, secondly, the Socio-Ecological-System framework was used to describe the current situation and analyze the interactions within the system and lastly, the X-curve model provided guidance in categorizing activities and policies that should be adapted, developed new or stopped. The international team showed how transition pathways for sustainable development can be developed in different contexts and scale levels, all over Europe. The resulting advice can be helpful to professionals active in regional development, on municipal, provincial, national, or European level.
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This paper reports on the first stage of a research project1) that aims to incorporate objective measures of physical activity into health and lifestyle surveys. Physical activity is typically measured with questionnaires that are known to have measurement issues, and specifically, overestimate the amount of physical activity of the population. In a lab setting, 40 participants wore four different sensors on five different body parts, while performing various activities (sitting, standing, stepping with two intensities, bicycling with two intensities, walking stairs and jumping). During the first four activities, energy expenditure was measured by monitoring heart rate and the gas volume of in‐ and expired O2 and CO2. Participants subsequently wore two sensor systems (the ActivPAL on the thigh and the UKK on the waist) for a week. They also kept a diary keeping track of their physical activities, work and travel hours. Machine learning algorithms were trained with different methods to determine which sensor and which method was best able to differentiate the various activities and the intensity with which they were performed. It was found that the ActivPAL had the highest overall accuracy, possibly because the data generated on the upper tigh seems to be best distinguishing between different types of activities and therefore led to the highest accuracy. Accuracy could be slightly increased by including measures of heartrate. For recognizing intensity, three different measures were compared: allocation of MET values to activities (used by ActivPAL), median absolute deviation, and heart rate. It turns out that each method has merits and disadvantages, but median absolute deviation seems to be the most promishing metric. The search for the best method of gauging intensity is still ongoing. Subsequently, the algorithms developed for the lab data were used to determine physical activity in the week people wore the devices during their everyday activities. It quickly turned out that the models are far from ready to be used on free living data. Two approaches are suggested to remedy this: additional research with meticulously labelled free living data, e.g., by combining a Time Use Survey with accelerometer measurements. The second is to focus on better determining intensity of movement, e.g., with the help of unsupervised pattern recognition techniques. Accuracy was but one of the requirements for choosing a sensor system for subsequent research and ultimate implementation of sensor measurement in health surveys. Sensor position on the body, wearability, costs, usability, flexibility of analysis, response, and adherence to protocol equally determine the choice for a sensor. Also from these additional points of view, the activPAL is our sensor of choice.
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