It is only by making an appropriate evaluation of the fidelity with which an intervention has been implemented that a viable assessment can be made of its contribution to outcomes, i.e., its effect on performance. Unless such an evaluation is made, it cannot be determined whether a lack of impact is due to poor implementation or inadequacies inherent in the programme itself, a so-called Type III error [11]; this is also addressed by the thesis of comprehensiveness [12]. It would also be unclear whether any positive outcomes produced by an intervention might be improved still further, if it were found that it had not been implemented fully.
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Primary research into interventions and their outcomes should therefore involve an evaluation of implementation fidelity if the true effect of the intervention is to be discerned. Moreover, evidence-based practitioners also need to be able to understand and quantify the fidelity with which they are implementing an intervention. Evidence-based practice assumes that an intervention is being implemented in full accordance with its published details. This is particularly important given the greater potential for inconsistencies in implementation of an intervention in real world rather than experimental conditions. Evidence-based practice therefore not only needs information from primary researchers about how to implement the intervention, if replication of the intervention is to be at all possible, it also needs a means of evaluating whether the programme is actually being implemented as the designers intended.
The concept of implementation fidelity is currently described and defined in the literature in terms of five elements that need to be measured [1, 2, 4]. These are: adherence to an intervention; exposure or dose; quality of delivery; participant responsiveness; and programme differentiation. There are certain overlaps here with the concept of process evaluation [15]. Within this conceptualisation of implementation fidelity, adherence is defined as whether "a program service or intervention is being delivered as it was designed or written" [4]. Dosage or exposure refers to the amount of an intervention received by participants; in other words, whether the frequency and duration of the intervention is as full as prescribed by its designers [1, 4]. For example, it may be that not all elements of the intervention are delivered, or are delivered less often than required. Coverage may also be included under this element, i.e., whether all the people who should be participating in or receiving the benefits of an intervention actually do so.
Program differentiation, the fifth aspect, is defined as "identifying unique features of different components or programs", and identifying "which elements of . . . programmes are essential", without which the programme will not have its intended effect [1]. Despite being viewed as an element of implementation fidelity by the literature, programme differentiation actually measures something distinct from fidelity. It is concerned with determining those elements that are essential for its success. This exercise is an important part of any evaluation of new interventions. It enables discovery of those elements that make a difference to outcomes and whether some elements are redundant. Such so-called "essential" elements may be discovered either by canvassing the designers of the intervention or, preferably, by "component analysis", assessing the effect of the intervention on outcomes and determining which components have the most impact [17]. This element would therefore be more usefully described as the "Identification of an intervention's essential components". This process may also have implications for implementation fidelity; if, for example, these essential components are the most difficult to implement, then this may then explain a lack of success afflicting the intervention.
The framework outlined in Figure 1 depicts the vital elements of implementation fidelity and their relationship to one another. The measurement of implementation fidelity is the measurement of adherence, i.e., how far those responsible for delivering an intervention actually adhere to the intervention as it is outlined by its designers. Adherence includes the subcategories of content, frequency, duration and coverage (i.e., dose). The degree to which the intended content or frequency of an intervention is implemented is the degree of implementation fidelity achieved for that intervention. The level achieved may be influenced or affected, (i.e., moderated) by certain other variables: intervention complexity, facilitation strategies, quality of delivery, and participant responsiveness. For example, the less enthusiastic participants are about an intervention, the less likely the intervention is to be implemented properly and fully.
The measurement of adherence to an intervention's predefined components can therefore be quantifiable: An evaluation to gauge how much of the intervention's prescribed content has been delivered, how frequently, and for how long. However, adherence may not require every single component of an intervention to be implemented. An intervention may also be implemented successfully, and meaningfully, if only the "essential" components of the model are implemented. However, the question remains about how to identify what is essential. One possible way to do this is to conduct a sensitivity analysis, or "component analysis", using implementation fidelity data and performance outcomes from different studies of the same intervention to determine which, if any, components or combination of components are essential, i.e., are prerequisite if the intervention is to have its desired effect. However, if essential components of an intervention are not known, then fidelity to the whole intervention is needed.
Identifying these essential components also provides scope for identifying adaptability to local conditions. An intervention cannot always be implemented fully in the real world. Local conditions may require it to be flexible and adaptable. Some specifications of interventions allow for local adaptation. Even if they do not explicitly do this, local adaptations may be made to improve the fit of the intervention within the local context. Indeed, the pro-adaptation perspective implies that successful interventions are those that adapt to local needs [22]. However, some argue that the case for local adaptation may well have been exaggerated, at least for interventions where the evidence does not necessarily support it [3]. The intermediate position is therefore that programme implementation can be flexible as long as there is fidelity to the so-called "essential" elements of an intervention. The absence of these elements would have significant adverse effects on the capacity of an intervention to achieve its goals. Indeed, without them it cannot meaningfully be said that an intervention has achieved high implementation fidelity.
There is also evidence that it is easier to achieve high fidelity of simple than complex interventions [1]. This may be because there are fewer "response barriers" when the model is simple [18]. Complex interventions have greater scope for variation in their delivery, and so are more vulnerable to one or more components not being implemented as they should. This has led to calls in some quarters for improving the recording and reporting of complex interventions to identify and address potential sources of heterogeneity in implementation [13, 14, 24]. Overall, research suggests that simple but specific interventions are more likely to be implemented with high fidelity than overly complex or vague ones. As such, the comprehensiveness and nature of an intervention's description may influence how far the programme successfully adheres to its prescribed details when implemented.
More facilitation strategies do not necessarily mean better implementation. A simple intervention may require very little in terms of training or guidance to achieve high implementation fidelity. A complex intervention by contrast may require extensive support strategies. There is therefore an issue of adequacy, and this may be determined by the relationship between facilitation strategies and the complexity of an intervention's description. The relationship between these potential moderators is discussed more fully below. Empirical research has yet to demonstrate whether facilitation strategies can indeed affect how well or how badly an intervention is implemented, but this should certainly be considered as a potential moderator of implementation fidelity.
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Automatic machine learning (AutoML) aims at automatically choosing the best configuration for machine learning tasks. However, a configuration evaluation can be very time consuming particularly on learning tasks with large datasets. This limitation usually restrains derivative-free optimization from releasing its full power for a fine configuration search using many evaluations. To alleviate this limitation, in this paper, we propose a derivative-free optimization framework for AutoML using multi-fidelity evaluations. It uses many lowfidelity evaluations on small data subsets and very few highfidelity evaluations on the full dataset. However, the lowfidelity evaluations can be badly biased, and need to be corrected with only a very low cost. We thus propose the Transfer Series Expansion (TSE) that learns the low-fidelity correction predictor efficiently by linearly combining a set of base predictors. The base predictors can be obtained cheaply from down-scaled and experienced tasks. Experimental results on real-world AutoML problems verify that the proposed framework can accelerate derivative-free configuration search significantly by making use of the multi-fidelity evaluations. 2ff7e9595c
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