Despite high prevalence of mental health problems among university students, there’s a gap between the need for help and the actual treatment received. This studyinvestigated disclosure on distress and hazardous alcohol use and help-seeking behavior in a sample of 1,791 students of a Dutch university of applied sciences.Students’ perceived public and personal stigma, and attitudes towards disclosure and help-seeking were assessed as possible predictors of disclosure and help-seekingbehavior. Results of the analysis of variance and logistic regression analysis indicated that perceived public and personal stigma did not predict disclosure and helpseeking behavior, but that attitudes towards disclosure and help-seeking did. Students with both distress and hazardous alcohol use have the least tendency to disclosetheir problems to family, friends or classmates, but at the same time they do tend to seek help. Disclosure and seeking help for mental health challenges are healthpromoting competencies that seem to need more attention in university students. Although further research needs to validate these findings, it is recommended topromote disclosure and help-seeking among students by investing in mental health literacy programs, to educate students about mental health issues, raise awarenesson available mental health services and their potential benefits.
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Artikel proefschrift Jos Dobber verschenen in Frontiers in Psychiatry 24 maart 2020: Background: Trials studying Motivational Interviewing (MI) to improve medication adherence in patients with schizophrenia showed mixed results. Moreover, it is unknown which active MI-ingredients are associated with mechanisms of change in patients with schizophrenia. To enhance the effect of MI for patients with schizophrenia, we studied MI's active ingredients and its working mechanisms. Methods: First, based on MI literature, we developed a model of potential active ingredients and mechanisms of change of MI in patients with schizophrenia. We used this model in a qualitative multiple case study to analyze the application of the active ingredients and the occurrence of mechanisms of change. We studied the cases of fourteen patients with schizophrenia who participated in a study on the effect of MI on medication adherence. Second, we used the Generalized Sequential Querier (GSEQ 5.1) to perform a sequential analysis of the MI-conversations aiming to assess the transitional probabilities between therapist use of MI-techniques and subsequent patient reactions in terms of change talk and sustain talk. Results: We found the therapist factor “a trusting relationship and empathy” important to enable sufficient depth in the conversation to allow for the opportunity of triggering mechanisms of change. The most important conversational techniques we observed that shape the hypothesized active ingredients are reflections and questions addressing medication adherent behavior or intentions, which approximately 70% of the time was followed by “patient change talk”. Surprisingly, sequential MI-consistent therapist behavior like “affirmation” and “emphasizing control” was only about 6% of the time followed by patient change talk. If the active ingredients were embedded in more comprehensive MI-strategies they had more impact on the mechanisms of change. Conclusions: Mechanisms of change mostly occurred after an interaction of active ingredients contributed by both therapist and patient. Our model of active ingredients and mechanisms of change enabled us to see “MI at work” in the MI-sessions under study, and this model may help practitioners to shape their MI-strategies to a potentially more effective MI.
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Background: For patients with coronary artery disease (CAD), smoking is an important risk factor for the recurrence of a cardiovascular event. Motivational interviewing (MI) may increase the motivation of the smokers to stop smoking. Data on MI for smoking cessation in patients with CAD are limited, and the active ingredients and working mechanisms of MI in smoking cessation are largely unknown. Therefore, this study was designed to explore active ingredients and working mechanisms of MI for smoking cessation in smokers with CAD, shortly after a cardiovascular event. Methods: We conducted a qualitative multiple case study of 24 patients with CAD who participated in a randomized trial on lifestyle change. One hundred and nine audio-recorded MI sessions were coded with a combination of the sequential code for observing process exchanges (SCOPE) and the motivational interviewing skill code (MISC). The analysis of the cases consisted of three phases: single case analysis, cross-case analysis, and cross-case synthesis. In a quantitative sequential analysis, we calculated the transition probabilities between the use of MI techniques by the coaches and the subsequent patient statements concerning smoking cessation. Results: In 12 cases, we observed ingredients that appeared to activate the mechanisms of change. Active ingredients were compositions of behaviors of the coaches (e.g., supporting self-efficacy and supporting autonomy) and patient reactions (e.g., in-depth self-exploration and change talk), interacting over large parts of an MI session. The composition of active ingredients differed among cases, as the patient process and the MI-coaching strategy differed. Particularly, change talk and self-efficacy appeared to stimulate the mechanisms of change “arguing oneself into change” and “increasing self-efficacy/confidence.”
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In order to design effective Persuasive Technology (PT) interventions, it is essential that designers understand the multitude of factors that lead to behavioral change, rather than guessing at a solution or imitating successful techniques without understanding why. The few available PT design frameworks solely distinguish behavioral determinants on an individual (micro) level (e.g., motivation), whereas successfully persuading a user is a multifaceted and complex task depending also on factors on a meso (e.g., available resources) and macro (e.g., social support and praise) level. We developed an analysis grid that enables PT designers to acknowledge the multifaceted character of determinants leading to behavioral change and select appropriate PT channels and strategies, preventing the failure of PT design. This analysis grid was validated in a case study in which we designed a PT intervention aimed at reporting minor crime incidents among citizens.
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The aim of the study was to evaluate whether multiple sclerosis (MS) is associated with risk of cataract or glaucoma. We conducted a population-based cohort study utilizing the UK General Practice Research Database (1987–2009) linked to the national hospital registry of England (1997–2008). Incident MS patients (5576 cases) were identified and each was matched to six patients without MS (controls) by age, gender, and practice. Cox proportional hazard models were used to estimate hazard ratios (HRs) of incident cataract and glaucoma in MS. Time-dependent adjustments were made for age, history of diseases and drug use.
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Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
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Patients undergoing major surgery are at risk of complications and delayed recovery. Prehabilitation has shown promise in improving postoperative outcomes. Offering prehabilitation by means of mHealth can help overcome barriers to participating in prehabilitation and empower patients prior to major surgery. We developed the Be Prepared mHealth app, which has shown potential in an earlier pilot study.
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At the beginning of the twenty first century obesity entered Dutch maternity care as a ‘new illness’ challenging maternity care professionals in providing optimal care for women with higher BMI’s. International research revealed that obese women had more perinatal problems than normal weight women. However, the effect of higher BMIs on perinatal outcomes had never been studied in women eligible for midwife-led primary care at the outset of their pregnancy. In the context of the Dutch maternity care system, it was not clear if obesity should be treated as a high-risk situation always requiring obstetrician-led care or as a condition that may lead to problems that could be detected in a timely manner in midwife-led care using the usual risk assessment tools. With the increased attention on obesity in maternity care there was also increased interest in GWG. Regarding GWG in the Netherlands, the effect of insufficient or excessive GWG on perinatal outcomes had never been studied and there were no validated guidelines for GWG. A midwife’s care for the individual woman in the context of the Dutch maternity care system - characterised by ‘midwife-led care if possible, obstetrician-led care if needed’ - is hampered by the lack of national multidisciplinary consensus regarding obesity and weight gain. Obesity has not yet been included in the OIL and local protocols contain varying recommendations. To enable sound clinical decisions and to offer optimal individual care for pregnant women in the Netherlands more insights in weight and weight gain in relation to perinatal outcomes are required. With this thesis the author intends to contribute to the body of knowledge on weight and weight gain to enhance optimal midwife-led primary care for the individual woman and to guide midwives’ clinical decision-making.
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Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated AI models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public.
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The aim of this project & work package is to develop a European action plan on mental health at work. A major and essential ingredient for this is the involvement of the relevant stakeholders and sharing experiences among them on the national and member state level. The Dutch Ministries of Health and Social Affairs and Employment have decided to participate in this “joint action on the promotion of mental health and well-being” with a specific focus on the work package directed at establishing a framework for action to promote taking action on mental health and well-being at workplaces at national level as well.
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