Empirical studies in the creative arts therapies (CATs; i.e., art therapy, dance/movement therapy, drama therapy, music therapy, psychodrama, and poetry/bibliotherapy) have grown rapidly in the last 10 years, documenting their positive impact on a wide range of psychological and physiological outcomes (e.g., stress, trauma, depression, anxiety, and pain). However, it remains unclear how and why the CATs have positive effects, and which therapeutic factors account for these changes. Research that specifically focuses on the therapeutic factors and/or mechanisms of change in CATs is only beginning to emerge. To gain more insight into how and why the CATs influence outcomes, we conducted a scoping review (Nstudies = 67) to pinpoint therapeutic factors specific to each CATs discipline, joint factors of CATs, and more generic common factors across all psychotherapy approaches. This review therefore provides an overview of empirical CATs studies dealing with therapeutic factors and/or mechanisms of change, and a detailed analysis of these therapeutic factors which are grouped into domains. A framework of 19 domains of CATs therapeutic factors is proposed, of which the three domains are composed solely of factors unique to the CATs: “embodiment,” “concretization,” and “symbolism and metaphors.” The terminology used in change process research is clarified, and the implications for future research, clinical practice, and CATs education are discussed.
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.
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.”
In order to achieve much-needed transitions in energy and health, systemic changes are required that are firmly based on the principles of regard for others and community values, while at the same time operating in market conditions. Social entrepreneurship and community entrepreneurship (SCE) hold the promise to catalyze such transitions, as they combine bottom-up social initiatives with a focus on financially viable business models. SCE requires a facilitating ecosystem in order to be able to fully realize its potential. As yet it is unclear in which way the entrepreneurial ecosystem for social and community entrepreneurship facilitates or hinders the flourishing and scaling of such entrepreneurship. It is also unclear how exactly entrepreneurs and stakeholders influence their ecosystem to become more facilitative. This research programme addresses these questions. Conceptually it integrates entrepreneurial ecosystem frameworks with upcoming theories on civic wealth creation, collaborative governance, participative learning and collective action frameworks.This multidisciplinary research project capitalizes on a unique consortium: the Dutch City Deal ‘Impact Ondernemen’. In this collaborative research, we enhance and expand current data collection efforts and adopt a living-lab setting centered on nine local and regional cases for collaborative learning through experimenting with innovative financial and business models. We develop meaningful, participatory design and evaluation methods and state-of-the-art digital tools to increase the effectiveness of impact measurement and management. Educational modules for professionals are developed to boost the abovementioned transition. The project’s learnings on mechanisms and processes can easily be adapted and translated to a broad range of impact areas.
Every year in the Netherlands around 10.000 people are diagnosed with non-small cell lung cancer, commonly at advanced stages. In 1 to 2% of patients, a chromosomal translocation of the ROS1 gene drives oncogenesis. Since a few years, ROS1+ cancer can be treated effectively by targeted therapy with the tyrosine kinase inhibitor (TKI) crizotinib, which binds to the ROS1 protein, impairs the kinase activity and thereby inhibits tumor growth. Despite the successful treatment with crizotinib, most patients eventually show disease progression due to development of resistance. The available TKI-drugs for ROS1+ lung cancer make it possible to sequentially change medication as the disease progresses, but this is largely a ‘trial and error’ approach. Patients and their doctors ask for better prediction which TKI will work best after resistance occurs. The ROS1 patient foundation ‘Stichting Merels Wereld’ raises awareness and brings researchers together to close the knowledge gap on ROS1-driven oncogenesis and increase the options for treatment. As ROS1+ lung cancer is rare, research into resistance mechanisms and the availability of cell line models are limited. Medical Life Sciences & Diagnostics can help to improve treatment by developing new models which mimic the situation in resistant tumor cells. In the current proposal we will develop novel TKI-resistant cell lines that allow screening for improved personalized treatment with TKIs. Knowledge of specific mutations occurring after resistance will help to predict more accurately what the next step in patient treatment could be. This project is part of a long-term collaboration between the ROS1 patient foundation ‘Stichting Merels Wereld’, the departments of Pulmonary Oncology and Pathology of the UMCG and the Institute for Life Science & Technology of the Hanzehogeschool. The company Vivomicx will join our consortium, adding expertise on drug screening in complex cell systems.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
