Well done on purchasing your all-new implementation scientist! Please note that coffee stains are part of the original design and there are no refunds.
How To Use
* Implementation science is the study of methods to promote the uptake and application of research findings. Your new implementation scientist can therefore provide support to achieve implementation outcomes, including: adoption, diffusion, acceptability, sustainability.
* We strongly advise that you deploy your model as early as possible during the research process. This will ensure that implementation factors are addressed throughout, including during design and evaluation. Users have reported problems similar to those encountered with health statisticians when they were first introduced, with the models only being activated at the end of the process “to sort that mess out”.
Why does my model keep saying the word context?
Implementation scientists love context. Context can be understood as the Who, Where, and When of how something is implemented. Neglect of contextual factors may cause models to overheat.
My model keeps trying to deliver Local Adaptations which are ruining my intervention fidelity. How do I get them to stop?
Local adaptation is not incompatible with replication and fidelity. Many studies increasingly recognise the need for tailoring of some delivery elements, whilst maintaining a standardised core of key ingredients.
What accessories does my model come with?
All models come pre-programmed with a selection of implementation theories and frameworks. The Theory-Builder specialisation can be purchased separately. Please note that some users have reported these units becoming stuck in “add/refine construct” mode without ever progressing to “test/apply theory”.
I asked my model how we can get the end users to engage with the research and they just said “Talk to them.” Is this an error?
Things To Avoid
* We must reiterate that your implementation scientist is best used early and often. As well as laying the neccesary groundwork for successful implementation in the future, lack of use can lead to them becoming dusty, which may aggravate the allergies on some models.
* Models are not restricted to the use of either qualitative or quantitative data alone, and may gravitate toward mixed-methods approaches.
* Models cannot magically create “pull” environments for “push” studies to deliver knowledge into. Problems with lack of fit can be avoided through early use and consideration of end user needs. Please consult the operating manual for further details.