Abstract Background: Children and adolescents in mental healthcare often perceive their care needs and necessary treatment differently from their clinicians. As such discordance between young patients and clinicians may obstruct treatment adherence and compromise treatment outcomes, it is important to understand the factors associated with it. We therefore investigated the factors associated with patient–clinician discordance with regard to care needs in various areas of functioning. Methods: A cross-sectional study involving 244 children/adolescents aged 6–18 participating with their clinicians in treatment at a specialized mental healthcare center. As a previous study conducted by our research group had found the greatest patient–clinician discordance in three CANSAS care needs—“mental health problems,” “information regarding diagnosis and/or treatment,” and “making and/or keeping friends”—we used univariable and multivariable statistics to investigate the factors associated with discordance regarding these three care needs. Results: patient–clinician discordance on the three CANSAS items was associated with child, parent, and family/social-context factors. Three variables were significant in each of the three final multivariable models: dangerous behavior towards self (child level); severity of psychiatric problems of the parent (parent level); and growing up in a single-parent household (family/social-context level). Conclusions: To deliver treatment most effectively and to prevent drop-out, it is important during diagnostic assessment and treatment planning to address the patient’s care needs at all three levels: child, parent and family/social context.
OBJECTIVE: To reach consensus on the most important biopsychosocial factors that influence functional capacity results in patients with chronic nonspecific musculoskeletal pain, arranged in the framework of the International Classification of Functioning, Disability and Health.DESIGN: Three-round, internet-based Delphi survey.SETTING: Not applicable.PARTICIPANTS: Participants were scientists, clinicians, and patients familiar with functional capacity testing. Scientists were invited through purposive sampling based on the number of relevant publications in peer-reviewed journals. The scientists recruited clinicians and patients through snowball sampling.INTERVENTIONS: Not applicable.MAIN OUTCOME MEASURES: Consensus was reached if at least moderate influence (25%) was achieved and an interquartile range of no more than 1 point was reached.RESULTS: Thirty-three scientists, 21 clinicians, and 21 patients from 9 countries participated. Participants reached consensus on 6 factors that can influence the outcome of the lifting test, having a median of severe influence (50%-95%): catastrophic thoughts and fear, patient adherence to "doctor's orders," internal and external motivation, muscle power, chronic pain behavior, and avoidance behavior. Motivation, chronic pain behavior, and sensation of pain were the top 3 factors affecting postural tolerance and repetitive movement functional capacity tests. Furthermore, participants reported 28 factors having a median of moderate influence (25%-49%) that could influence the outcome of lifting, postural tolerance, and repetitive movement tests.CONCLUSIONS: Overall, chronic pain behavior, motivation, and sensation of pain are the main factors that can influence functional capacity results. We recommend that scientists and clinicians, respectively, consider the most important factors when planning future studies and when interpreting functional capacity test results.
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Background: The most common reason for caesarean section (CS) is repeat CS following previous CS. Vaginal birth after caesarean section (VBAC) rates vary widely in different healthcare settings and countries. Obtaining deeper knowledge of clinicians’ views on VBAC can help in understanding the factors of importance for increasing VBAC rates. Interview studies with clinicians and women in three countries with high VBAC rates (Finland, Sweden and the Netherlands) and three countries with low VBAC rates (Ireland, Italy and Germany) are part of ‘OptiBIRTH’, an ongoing research project. The study reported here is based on interviews in high VBAC countries. The aim of the study was to investigate the views of clinicians working in countries with high VBAC rates on factors of importance for improving VBAC rates. Methods: Individual (face-to-face or telephone) interviews and focus group interviews with clinicians (in different maternity care settings) in three countries with high VBAC rates were conducted during 2012–2013. In total, 44 clinicians participated: 26 midwives and 18 obstetricians. Five central questions about VBAC were used and interviews were analysed using content analysis. The analysis was performed in each country in the native language and then translated into English. All data were then analysed together and final categories were validated in each country. Results: The findings are presented in four main categories with subcategories. First, a common approach is needed, including: feeling confident with VBAC, considering VBAC as the first alternative, communicating well, working in a team, working in accordance with a model and making agreements with the woman. Second, obstetricians need to make the final decision on the mode of delivery while involving women in counselling towards VBAC. Third, a woman who has a previous CS has a similar need for support as other labouring women, but with some extra precautions and additional recommendations for her care. Finally, clinicians should help strengthen women’s trust in VBAC, including building their trust in giving birth vaginally, recognising that giving birth naturally is an empowering experience for women, alleviating fear and offering extra visits to discuss the previous CS, and joining with the woman in a dialogue while leaving the decision about the mode of birth open. Conclusions: This study shows that, according to midwives and obstetricians from countries with high VBAC rates, the important factors for improving the VBAC rate are related to the structure of the maternity care system in the country, to the cooperation between midwives and obstetricians, and to the care offered during pregnancy and birth. More research on clinicians’ perspectives is needed from countries with low, as well as high, VBAC rates.
MULTIFILE
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).
