Fidelity and moderating factors in complex interventions: a case study of a continuum of care program for frail elderly people in health and social care

<p>Abstract</p> <p>Background</p> <p>Prior studies measuring fidelity of complex interventions have mainly evaluated adherence, and not taken factors affecting adherence into consideration. A need for studies that clarify the concept of fidelity and the function of factors moderating fidelity has been emphasized. The aim of the study was to systematically evaluate implementation fidelity and possible factors influencing fidelity of a complex care continuum intervention for frail elderly people.</p> <p>Methods</p> <p>The intervention was a systematization of the collaboration between a nurse with geriatric expertise situated at the emergency department, the hospital ward staff, and a multi-professional team with a case manager in the municipal care services for older people. Implementation was evaluated between September 2008 and May 2010 with observations of work practices, stakeholder interviews, and document analysis according to a modified version of The Conceptual Framework for Implementation Fidelity.</p> <p>Results</p> <p>A total of 16 of the 18 intervention components were to a great extent delivered as planned, while some new components were added to the model. No changes in the frequency or duration of the 18 components were observed, but the dose of the added components varied over time. Changes in fidelity were caused in a complex, interrelated fashion by all the moderating factors in the framework, i.e., context, staff and participant responsiveness, facilitation, recruitment, and complexity.</p> <p>Discussion</p> <p>The Conceptual Framework for Implementation Fidelity was empirically useful and included comprehensive measures of factors affecting fidelity. Future studies should focus on developing the framework with regard to how to investigate relationships between the moderating factors and fidelity over time.</p> <p>Trial registration</p> <p>ClinicalTrials.gov, <a href="http://www.clinicaltrials.gov/ct2/show/NCT01260493">NCT01260493</a>.</p

Face averages enhance user recognition for smartphone security

Our recognition of familiar faces is excellent, and generalises across viewing conditions. However, unfamiliar face recognition is much poorer. For this reason, automatic face recog- nition systems might benefit from incorporating the advantages of familiarity. Here we put this to the test using the face verification system available on a popular smartphone (the Samsung Galaxy). In two experiments we tested the recognition performance of the smart- phone when it was encoded with an individual’s ‘face-average’– a representation derived from theories of human face perception. This technique significantly improved performance for both unconstrained celebrity images (Experiment 1) and for real faces (Experiment 2): users could unlock their phones more reliably when the device stored an average of the user’s face than when they stored a single image. This advantage was consistent across a wide variety of everyday viewing conditions. Furthermore, the benefit did not reduce the re- jection of imposter faces. This benefit is brought about solely by consideration of suitable representations for automatic face recognition, and we argue that this is just as important as development of matching algorithms themselves. Wepropose that this representation could significantly improve recognition rates in everyday settings. Introductio

Instrumentation issues in implementation science

Background Like many new fields, implementation science has become vulnerable to instrumentation issues that potentially threaten the strength of the developing knowledge base. For instance, many implementation studies report findings based on instruments that do not have established psychometric properties. This article aims to review six pressing instrumentation issues, discuss the impact of these issues on the field, and provide practical recommendations. Discussion This debate centers on the impact of the following instrumentation issues: use of frameworks, theories, and models; role of psychometric properties; use of ‘home-grown’ and adapted instruments; choosing the most appropriate evaluation method and approach; practicality; and need for decision-making tools. Practical recommendations include: use of consensus definitions for key implementation constructs; reporting standards (e.g., regarding psychometrics, instrument adaptation); when to use multiple forms of observation and mixed methods; and accessing instrument repositories and decision aid tools. Summary This debate provides an overview of six key instrumentation issues and offers several courses of action to limit the impact of these issues on the field. With careful attention to these issues, the field of implementation science can potentially move forward at the rapid pace that is respectfully demanded by community stakeholders