Competent delivery of interventions in child and youth social care is important, due to the direct effect on client outcomes. This is acknowledged in evidence-based interventions (EBI) when, post-training, continued support is available to ensure competent delivery of the intervention. In addition to EBI, practice-based interventions (PBI) are used in the Netherlands. The current paper discusses to what extent competent delivery of PBI can be influenced by introducing supervision for professionals. This study used a mixed-method design: (1) A small-n study consisting of six participants in a non-concurrent multiple baseline design (MBL). Professionals were asked to record conversations with clients during a baseline period (without supervision) and an intervention period (with supervision). Visual inspection, the non-overlap of all pairs (NAP), and the Combinatorial Inference Technique (CIT) scores were calculated. (2) Qualitative interviews with the six participants, two supervisors, and one lead supervisor focused on the feasibility of the supervision. Four of six professionals showed improvement in treatment fidelity or one of its sub-scales. Had all participants shown progress, this could have been interpreted as an indication that targeted support of professionals contributes to increasing treatment integrity. Interviews have shown that supervision increased the professionals’ enthusiasm, self-confidence, and awareness of working with the core components of the intervention. The study has shown that supervision can be created for PBI and that this stimulates professionals to work with the core components of the intervention. The heterogeneous findings on intervention fidelity can be the result of supervision being newly introduced.
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The TOP program is a fully implemented responsive parenting intervention for very preterm born infants. Fidelity monitoring of interventions is important for preserving program adherence, impact outcomes and to make evidence-based adaptations. The aim of this study was to develop a fidelity tool for the TOP program following an iterative and co-creative process and subsequently evaluate the reliability of the tool. Three consecutive phases were carried out. Phase I: Initial development and pilot testing two methods namely self-report and video based observation. Phase II: Adaptations and refinements. Phase III: Evaluation of the psychometric properties of the tool based on 20 intervention videos rated by three experts.The interrater reliability of the adherence and competence subscales was good (ICC.81 to .84) and varied from moderate to excellent for specific items (ICC between .51 and .98). The FITT displayed a high correlation (Spearman’s rho.79 to.82) between the subscales and total impression item. The co-creative and iterative process resulted in a clinical useful and reliable tool for evaluating fidelity in the TOP program. This study offers insights in the practical steps in the development of a fidelity assessment tool which can be used by other intervention developers.
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This article describes a measure developed to assess fidelity of working with the Boston University approach to Psychiatric Rehabilitation (BPR) in Dutch mental health care. The instrument is intended to measure and improve BPR adherence and clinician competence on an individual level and within individual rehabilitation processes. https://www.ncbi.nlm.nih.gov/pubmed/28771017
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Communicatieprofessionals geven aan dat organisaties geconfronteerd worden met een almaar complexere samenleving en daarmee het overzicht verloren hebben. Zo’n overzicht, een ‘360 graden blik’, is echter onontbeerlijk. Dit vooral, aldus diezelfde communicatieprofessionals, omdat dan eerder kan worden opgemerkt wanneer de legitimiteit van een organisatie ter discussie staat en zowel tijdiger als adequater gereageerd kan worden. Op dit moment is het echter nog zo dat een reactie pas op gang komt als zaken reeds in een gevorderd stadium verkeren. Onderstromen blijven onderbelicht, als ze niet al geheel onzichtbaar zijn. Een van de verklaringen hiervoor is de grote rol van sociale media in de publieke communicatie van dit moment. Die media produceren echter zoveel data dat communicatieprofessionals daartegenover machteloos staan. De enige oplossing is automatisering van de selectie en analyse van die data. Helaas is men er tot op heden nog niet in geslaagd een brug te slaan tussen het handwerk van de communicatieprofessional en de vele mogelijkheden van een datagedreven aanpak. Deze brug dan wel de vertaling van de huidige praktijk naar een hogere technisch niveau staat centraal in dit onderzoeksproject. Daarbij gaat het in het bijzonder om een vroegtijdige herkenning van potentiële issues, in het bijzonder met betrekking tot geruchtvorming en oproepen tot mobilisatie. Met discoursanalyse, AI en UX Design willen we interfaces ontwikkelen die zicht geven op die onderstromen. Daarbij worden transcripten van handmatig gecodeerde discoursanalytische datasets ingezet voor AI, in het bijzonder voor de clustering en classificatie van nieuwe data. Interactieve datavisualisaties maken die datasets vervolgens beter doorzoekbaar terwijl geautomatiseerde patroon-classificaties de communicatieprofessional in staat stellen sociale uitingen beter in te schatten. Aldus wordt richting gegeven aan handelingsperspectieven. Het onderzoek voorziet in de oplevering van een high fidelity ontwerp en een handleiding plus training waarmee analisten van newsrooms en communicatieprofessionals daadwerkelijk aan de slag kunnen gaan.
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.
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.
