People with psychiatric disabilities frequently experience difficulties in pursuing higher education. For instance, the nature of their disability and its treatment, stigmatization and discrimination can be overwhelming obstacles. These difficulties can eventually lead to early school leaving and consequently to un- or underemployment. Unfortunately, support services for (future) students with psychiatric disabilities are often not available at colleges and universities or at mental health organizations.For the social inclusion and (future) labor opportunities of people with psychiatric disabilities it is of the utmost importance that they have better access to higher education, and are able to complete such study successfully. Supported Education is a means to reach these goals. Supported Education is defined as the provision of individualized, practical support and instruction to assist people with psychiatric disabilities to achieve their educational goals (Anthony, Cohen, Farkas, & Gagne, 2002).The main aim of the ImpulSE project (see Appendix 1 for information about the project's organization) was the development of a toolkit for Supported Education services for (future) students with psychiatric disabilities. The toolkit is based upon needs and resources assessments from the four participating countries, as well as good practices from these.Secondly, a European network of Supported Education (ENSEd) is initiated, starting with a first International Conference on Supported Education. The aim of ENSEd is to raise awareness in the EU about the educational needs of (future) students with psychiatric disabilities and for services that help to remove the barriers for this target group.The toolkit is aimed at students’ counselors, trainers, teachers and tutors, mental health managers and workers, and local authority officials involved in policymaking concerning people with psychiatric disabilities. It enables field workers to improve guidance and counseling to (future) students with psychiatric disabilities, supporting them in their educational careers.In the Netherlands alone, it is estimated that six per cent of the total student population suffers from a psychiatric disability—that is, a total of 40,000 students. On a European scale, the number of students with a psychiatric disability is therefore considerably high. We hope that through the project these students will be better empowered to be successful in their educational careers and that their chances in the labor market and their participation in society at large will be improved.
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Psychiatric-mental health nurse practitioners (PMHNPs) in the Netherlands have been allowed to perform the role of coordinating practitioner (CP) since 2018. This role is reserved for mental health care specialists who are trained and qualified at the master's degree level. Earlier studies have not addressed how PMHNPs perform that role and what mechanisms and contextual factors determine their performance. This understanding could help optimize their performance in this role and promote effective deployment of PMHNPs in mental health care.
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Because dental health and oral pathology may affect forensic psychiatric patients' well being, it is important to be able to assess oral health related quality of life (OH-QoL) in these patients. Two studies were conducted among Dutch forensic psychiatric male patients to assess the psychometric properties and some potential predictors of the Oral Health Impact Profile-14 (OHIP-14) as a measure of OH-QoL. Study 1 involved 40 patients who completed the OHIP-14 before receiving professional dental care and were retested 3 months later. The internal consistency was good, the test-retest correlations were fair, and over the 3 months follow-up no significant changes in OH-QoL were observed. Study 2 consisted of 39 patients who completed an improved version of the original OHIP-14, as well as measures to validate of the OHIP. Dental anxiety and unhealthy dentition jointly explained 26.7% of the variance in OH-QoL, and the better patients performed their oral hygiene behavior, the better their OH-QoL. It is concluded that the Dutch OHIP-14 is a useful instrument, and that nurses, especially in forensic nursing, should pay particularly attention to dental anxiety when encouraging patients to visit OH professionals and to perform adequate oral hygiene self-care.
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
