Search results

Fidelity data in clinical trials are not only necessary for appraising the internal and external validity, but also could provide useful insights how to improve the application of an intervention in everyday settings. The purpose of this paper is to understand the current literature of fidelity measurements in complex interventions for people with intellectual disabilities (ID) and behaviours that challenge.

The electronic databases MEDLINE, Embase, PsycINFO, Web of Science and CINAHL Plus were searched for studies published between 1990 to 2017 that have mentioned fidelity in randomised controlled trials of complex interventions for people with ID and behaviours that challenge based on positive behaviour support or applied behaviour analysis principles. The authors also searched the grey literature and reference lists.

Five randomised controlled trials were included in the review. The authors found variable and inconsistent fidelity measurements reported in the studies. The most frequently provided fidelity elements found in four out of five studies were adherence of implementation, dose and some aspects of quality of delivery.

Research recommendations for a standardised approach of measuring fidelity in such studies are suggested.

The first review of such type that confirms the paucity of research measuring fidelity in complex interventions in this population.

Strategies for accelerated multi-objective optimization of aerodynamic surfaces are investigated, including the possibility of exploiting surrogate modeling techniques for computational fluid dynamic (CFD)-driven design speedup of such surfaces. The purpose of this paper is to reduce the overall optimization time.

An algorithmic framework is described that is composed of: a search space reduction, fast surrogate models constructed using variable-fidelity CFD models and co-Kriging, and Pareto front refinement. Numerical case studies are provided demonstrating the feasibility of solving real-world problems involving multi-objective optimization of transonic airfoil shapes and accurate CFD simulation models of such surfaces.

It is possible, through appropriate combination of surrogate modeling techniques and variable-fidelity models, to identify a set of alternative designs representing the best possible trade-offs between conflicting design objectives in a realistic time frame corresponding to a few dozen of high-fidelity CFD simulations of the respective surfaces.

The proposed aerodynamic design optimization algorithmic framework is novel and holistic. It proved useful for fast design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search space, which is extremely challenging when using conventional methods due to the excessive computational cost.

Dual-rotor wind turbines (DRWTs) are a novel type of wind turbines that can capture more power than their single-rotor counterparts. Because their surrounding flow fields are complex, evaluating a DRWT design requires accurate predictive simulations, which incur high computational costs. Currently, there does not exist a design optimization framework for DRWTs. Since the design optimization of DRWTs requires numerous model evaluations, the purpose of this paper is to identify computationally efficient design approaches.

Several algorithms are compared for the design optimization of DRWTs. The algorithms vary widely in approaches and include a direct derivative-free method, as well as three surrogate-based optimization methods, two approximation-based approaches and one variable-fidelity approach with coarse discretization low-fidelity models.

The proposed variable-fidelity method required significantly lower computational cost than the derivative-free and approximation-based methods. Large computational savings come from using the time-consuming high-fidelity simulations sparingly and performing the majority of the design space search using the fast variable-fidelity models.

Due the complex simulations and the large number of designable parameters, the design of DRWTs require the use of numerical optimization algorithms. This work presents a novel and efficient design optimization framework for DRWTs using computationally intensive simulations and variable-fidelity optimization techniques.

This paper aims to investigates several design strategies to solve multi-objective aerodynamic optimization problems using high-fidelity simulations. The purpose is to find strategies which reduce the overall optimization time while still maintaining accuracy at the high-fidelity level.

Design strategies are proposed that use an algorithmic framework composed of search space reduction, fast surrogate models constructed using a combination of physics-based surrogates and kriging and global refinement of the Pareto front with co-kriging. The strategies either search the full or reduced design space with a low-fidelity model or a physics-based surrogate.

Numerical investigations of airfoil shapes in two-dimensional transonic flow are used to characterize and compare the strategies. The results show that searching a reduced design space produces the same Pareto front as when searching the full space. Moreover, as the reduced space is two orders of magnitude smaller (volume-wise), the number of required samples to setup the surrogates can be reduced by an order of magnitude. Consequently, the computational time is reduced from over three days to less than half a day.

The proposed design strategies are novel and holistic. The strategies render multi-objective design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces computationally tractable.

Two common methods in community settings of assessing program fidelity, a critical implementation component for program effectiveness, are video and audio recordings of sessions. This paper aims to examine how these two methods compared when used for a home-based behavioral parenting-training model (SafeCare®).

Twenty-five SafeCare video-recorded sessions between home visitors and parents were scored by trained raters either using the video or audio-only portions of recordings. Sessions were coded using fidelity checklists, with items (n = 33) classified as one of two fidelity aspects, content [delivery of program components (n = 15)], or process [communication and rapport building (n = 11)]. Seven items were considered to overlap between constructs. Items were coded as having been done or not done appropriately. Coders rated items as “technological limitation” when scoring methods hindered coding. Analyses compared percent agreement and disagreement between audio and video coders.

Overall agreement between coders was 72.12%. Levels of agreement were higher for content items (M = 80.89%, SD = 19.68) than process items (58.54%, SD = 34.41). Disagreements due to technology limitations among audio coders were noted among 15 items; particularly, higher levels of disagreement were seen among process items (42.42%) than content items (9.64%).

Compared to video, fidelity monitoring via audio recordings was associated with some loss of process-related fidelity. However, audio recordings could be sufficient with supplements such as participant surveys, to better capture process items. Research should also examine how content and process fidelity relate to changes in family behavior to further inform optimal fidelity monitoring methods for program use.

The purpose of this paper is to explore the implementation of a theoretically grounded coach education training programme for youth football coaches in Norway, through observational methods. In particular, it focuses on implementation fidelity and programme adaptation, and possible differences between the coach educators (CEs) according to their level of experience.

Implementation fidelity and programme adaptations for seven CEs were explored through the use of a fidelity scale and in-depth qualitative analyses. Participant responsiveness was applied to inform the observational analyses.

Results showed that most of the programme was implemented with moderate to high fidelity, and that it was adapted when delivered. Most of the adaptations seemed to be positive, aligning with the programme’s theoretical foundation and goals. A few negative and neutral adaptations also occurred. The most experienced CEs seemed to deliver the programme with highest fidelity and they also made most positive adaptations. The findings, supported by the participants’ evaluation of the programme delivery, indicate that these CEs also delivered the programme with high quality.

In the field of implementation research there is a need for further studies applying observational methods to explore programme effectiveness in relation to implementation fidelity, adaptations made and the quality of programme delivery.

This study applies observational methods to evaluate the fidelity of implementation and adaptations made when implementing a coach education programme, and based on these findings quality of delivery is discussed.

The purpose of this paper is to demonstrate the importance of considering both fidelity and adaptation in assessing the implementation of evidence‐based programs.

The current study employs a multi‐method strategy to understand two dimensions of implementation (fidelity and adaptation) in the Strong African American Families (SAAF) program. Data were video recordings of program delivery and pre‐test and post‐test interviews from the efficacy trial. Multilevel regression in Mplus was used to assess the impact of fidelity to the manual, coded by independent observers, on racial socialization outcomes. One activity on racial socialization, a core component of the program, was selected for an in‐depth examination using conversation analysis (a qualitative method of analyzing talk in interactions).

Results of the quantitative analyses demonstrated that fidelity of the selected activity was associated with increases in parent's use of racial socialization from pre‐test to post‐test, but only when participant attendance was included in the model. Results of the qualitative analyses demonstrated that facilitators were making adaptations to the session and that these adaptations appeared to be in line with cultural competence.

The development of quantitative fidelity measures can be problematic, with many decision points to consider. The current study contributes to the evidence base to develop a quantitative measure of adaptation for family‐based parenting programs.

Many researchers examining implementation of evidence‐based programs consider fidelity and adaptation to be polar ends of a single spectrum. This paper provides evidence for the importance of examining each independently.

As [Alexander Graham] Bell raced to perfect his telephone, he was also writing up specifications to be filed with the United States Patent Office in Washington. On March 7, 1876, he was issued patent number 174,465. Meanwhile, Bell had discovered that a wire vibrated by the voice while partially immersed in a conducting liquid, like mercury, could be made to vary its resistance and produce an undulating current. In other words, human speech could be transmitted over a wire. On March 10, 1876, as he and Mr. Watson set out to test this finding, Bell knocked over what they were using as a transmitting liquid – battery acid. Reacting to the spilled acid, Mr. Bell is alleged to have shouted, “Mr. Watson, come here. I want you!” (PBS, 2003)

This paper aims to present a multi-fidelity surrogate-based optimization (MFSBO) method for computationally accurate and efficient design of microfluidic concentration gradient generators (µCGGs).

Cokriging-based multi-fidelity surrogate model (MFSM) is constructed to combine data with varying fidelities and computational costs to accelerate the optimization process and improve design accuracy. An adaptive sampling approach based on parallel infill of multiple low-fidelity (LF) samples without notably adding computation burden is developed. The proposed optimization framework is compared with a surrogate-based optimization (SBO) method that relies on data from a single source, and a conventional multi-fidelity adaptive sampling and optimization method in terms of the convergence rate and design accuracy.

The results demonstrate that proposed MFSBO method allows faster convergence and better designs than SBO for all case studies with 49% more reduction in the objective function value on average. It is also found that parallel infill (MFSBO-4) with four LF samples, enables more robust, efficient and accurate designs than conventional multi-fidelity infill (MFSBO-1) that only adopts one LF sample during each iteration for more complex optimization problems.

A MFSM based on cokriging method is constructed to utilize data with varying fidelities, accuracies and computational costs for µCGG design. A parallel infill strategy based on multiple infill criteria is developed to accelerate the convergence and improve the design accuracy of optimization. The proposed methodology is proved to be a feasible method for µCGG design and its computational efficiency is verified.