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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Abstract Summary: Program fidelity instruments are a key ingredient for clinical supervision and implementation as well as effectiveness studies. This study examines the factor structure of the Functional Family Parole services Global Rating Measure (FFP-GRM); the program fidelity instrument of Functional Family Parole services for case management in youth parole, child protection and child welfare services. Between October 2012 and February 2015, program fidelity was measured with the FFP-GRM by Functional Family Parole supervisors. Confirmatory factor analysis was performed on 380 cases and internal consistency reliability coefficients were calculated. Findings: Confirmatory factor analyses showed that the 33-item and four-factor model of the FFP-GRM achieved a good fit to the data. Internal validity testing results showed that subscale Cronbach’s a ranged between .82 and .90. Applications: Findings affirm a good fit to the data and a good-to-excellent internal consistency of the FFP-GRM, which is considered sufficient to justify its use. The results are discussed with regard to the use of fidelity instruments for both clinical and research purposes.
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BACKGROUND: Although the importance of evaluating implementation fidelity is acknowledged, little is known about heterogeneity in fidelity over time. This study aims to generate insight into the heterogeneity in implementation fidelity trajectories of a health promotion program in multidisciplinary settings and the relationship with changes in patients' health behavior.METHODS: This study used longitudinal data from the nationwide implementation of an evidence-informed physical activity promotion program in Dutch rehabilitation care. Fidelity scores were calculated based on annual surveys filled in by involved professionals (n = ± 70). Higher fidelity scores indicate a more complete implementation of the program's core components. A hierarchical cluster analysis was conducted on the implementation fidelity scores of 17 organizations at three different time points. Quantitative and qualitative data were used to explore organizational and professional differences between identified trajectories. Regression analyses were conducted to determine differences in patient outcomes.RESULTS: Three trajectories were identified as the following: 'stable high fidelity' (n = 9), 'moderate and improving fidelity' (n = 6), and 'unstable fidelity' (n = 2). The stable high fidelity organizations were generally smaller, started earlier, and implemented the program in a more structured way compared to moderate and improving fidelity organizations. At the implementation period's start and end, support from physicians and physiotherapists, professionals' appreciation, and program compatibility were rated more positively by professionals working in stable high fidelity organizations as compared to the moderate and improving fidelity organizations (p < .05). Qualitative data showed that the stable high fidelity organizations had often an explicit vision and strategy about the implementation of the program. Intriguingly, the trajectories were not associated with patients' self-reported physical activity outcomes (adjusted model β = - 651.6, t(613) = - 1032, p = .303).CONCLUSIONS: Differences in organizational-level implementation fidelity trajectories did not result in outcome differences at patient-level. This suggests that an effective implementation fidelity trajectory is contingent on the local organization's conditions. More specifically, achieving stable high implementation fidelity required the management of tensions: realizing a localized change vision, while safeguarding the program's standardized core components and engaging the scarce physicians throughout the process. When scaling up evidence-informed health promotion programs, we propose to tailor the management of implementation tensions to local organizations' starting position, size, and circumstances.TRIAL REGISTRATION: The Netherlands National Trial Register NTR3961 . Registered 18 April 2013.
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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.
