Measurement of a model of implementation for health care: toward a testable theory

<p>Abstract</p> <p>Background</p> <p>Greenhalgh et al. used a considerable evidence-base to develop a comprehensive model of implementation of innovations in healthcare organizations [1]. However, these authors did not fully operationalize their model, making it difficult to test formally. The present paper represents a first step in operationalizing Greenhalgh et al.’s model by providing background, rationale, working definitions, and measurement of key constructs.</p> <p>Methods</p> <p>A systematic review of the literature was conducted for key words representing 53 separate sub-constructs from six of the model’s broad constructs. Using an iterative process, we reviewed existing measures and utilized or adapted items. Where no one measure was deemed appropriate, we developed other items to measure the constructs through consensus.</p> <p>Results</p> <p>The review and iterative process of team consensus identified three types of data that can been used to operationalize the constructs in the model: survey items, interview questions, and administrative data. Specific examples of each of these are reported.</p> <p>Conclusion</p> <p>Despite limitations, the mixed-methods approach to measurement using the survey, interview measure, and administrative data can facilitate research on implementation by providing investigators with a measurement tool that captures most of the constructs identified by the Greenhalgh model. These measures are currently being used to collect data concerning the implementation of two evidence-based psychotherapies disseminated nationally within Department of Veterans Affairs. Testing of psychometric properties and subsequent refinement should enhance the utility of the measures.</p

Life-History Responses to the Altitudinal Gradient

We review life-history variation along elevation in animals and plants and illustrate its drivers, mechanisms and constraints. Elevation shapes life histories into suites of correlated traits that are often remarkably convergent among organisms facing the same environmental challenges. Much of the variation observed along elevation is the result of direct physiological sensitivity to temperature and nutrient supply. As a general rule, alpine populations adopt ‘slow’ life cycles, involving long lifespan, delayed maturity, slow reproductive rates and strong inversions in parental care to enhance the chance of recruitment. Exceptions in both animals and plants are often rooted in evolutionary legacies (e.g. constraints to prolonging cycles in obligatory univoltine taxa) or biogeographic history (e.g. location near trailing or leading edges). Predicting evolutionary trajectories into the future must take into account genetic variability, gene flow and selection strength, which define the potential for local adaptation, as well as the rate of anthropogenic environmental change and species’ idiosyncratic reaction norms. Shifts up and down elevation in the past helped maintain genetic differentiation in alpine populations, with slow life cycles contributing to the accumulation of genetic diversity during upward migrations. Gene flow is facilitated by the proximity of neighbouring populations, and global warming is likely to move fast genotypes upwards and reduce some of those constraints dominating alpine life. Demographic buffering or compensation may protect local alpine populations against trends in environmental conditions, but such mechanisms may not last indefinitely if evolutionary trajectories cannot keep pace with rapid changes.Peer reviewe