Alliance has been shown to predict treatment outcome in family-involved treatment for youth problems in several studies.However, meta-analytic research on alliance in family-involved treatment is scarce, and to date, no meta-analytic study on the alliance–outcome association in this field has paid attention to moderating variables. We included 28 studies reporting on the alliance–outcome association in 21 independent study samples of families receiving family-involved treatment for youth problems (N= 2126 families,Mage youth ranging from 10.6 to 16.1). We performed three multilevel meta-analyses of theassociations between three types of alliance processes and treatment outcome, and of several moderator variables. The quality of the alliance was significantly associated with treatment outcome (r= .183,p< .001). Correlations were significantly stronger when alliance scores of different measurement moments were averaged or added, when families were help-seekingrather than receiving mandated care and when studies included younger children. The correlation between alliance improvement and treatment outcome just failed to reached significance (r= .281,p= .067), and no significant correlation was found between split alliances and treatment outcome (r= .106,p= .343). However, the number of included studies reporting onalliance change scores or split alliances was small. Our findings demonstrate that alliance plays a small but significant role in the effectiveness of family-involved treatment. Future research should focus on investigating the more complex systemic aspects of alliance to gain fuller understanding of the dynamic role of alliance in working with families
MULTIFILE
This research focuses on dinner conversations in family-style group care. Children, who cannot live with their biological families anymore, are given shelter in these family-style group care settings. For the development of an attachment relationship between children and their Professional Foster Parents (PFPs), it is important that the children feel that they are listened to in order to get an affective and intimate relationship with the parents. In this conversation-analytic research we analysed PFPs’ involvement in multiple activities simultaneously, namely listening and eating, which is referred to as ‘multi-activity’. The analyses have shown systematic ways in which PFPs coordinate their involvement in the activities of ‘doing’ listening and eating, which are (i) when parents avert their gaze from the telling child, they break the social rule which states that hearers need to look at speakers during the telling. We found that when averting their gaze, PFPs do head nods and linguistic means or positioning their bodies in the direction of the telling child. This research contributes to knowledge about interaction between adolescents and PFPs. It further contributes to knowledge about how human beings are able to coordinate multiple activities simultaneously. This is the accepted version (post-print) of the article.
• The combination of coping with mental health problems and caring for children makes parents vulnerable.• Family-centred practice can help to maintain and strengthen important family relationships, and to identify and enhance the strengths of parents with a mental illness, thus contributing to their recovery.• Parents with mental illness find strength for parenting in several ways. They feel responsible, and this helps them to stay alert while parenting; parenthood also offers a basis for social participation.• Dedication to the parental role provides a focus; parents develop strengths and skills as they find a balance between attending to their own lives and caring for their children, and parenting prompts them to find adequate sources of support and leads to a valued identity.• Practitioners can support parents with mental health problems to set and address parenting related goals.
Significant Others, family care, substance abuse, addiction, substance use disorder, Concerned significant others of a person with substance use disorder face psychological, social and financial problems caused by the subtance abuse of their loved one. Tradionally health care orginizations focus on the person with substance use disorder and pay less attention to their concerned significant other. In the Netherlands there is less information available about concerned significant others of persons with substance abuse. To develop a family care aproach for the significant other it's necessary to provide insight in the charasteristics of the concerned significant others of persons with substance use disorder.
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).
