Uncovering the mechanisms of research capacity development in health and social care: a realist synthesis

BACKGROUND: Research capacity development (RCD) is considered fundamental to closing the evidence-practice gap, thereby contributing to health, wealth and knowledge for practice. Numerous frameworks and models have been proposed for RCD, but there is little evidence of what works for whom and under what circumstances. There is a need to identify mechanisms by which candidate interventions or clusters of interventions might achieve RCD and contribute to societal impact, thereby proving meaningful to stakeholders. METHODS: A realist synthesis was used to develop programme theories for RCD. Structured database searches were conducted across seven databases to identify papers examining RCD in a health or social care context (1998-2013). In addition, citation searches for 10 key articles (citation pearls) were conducted across Google Scholar and Web of Science. Of 214 included articles, 116 reported on specific interventions or initiatives or their evaluation. The remaining 98 articles were discussion papers or explicitly sought to make a theoretical contribution. A core set of 36 RCD theoretical and conceptual papers were selected and analysed to generate mechanisms that map across macro contexts (individual, team, organisational, network). Data were extracted by means of 'If-Then' statements into an Excel spreadsheet. Models and frameworks were deconstructed into their original elements. RESULTS: Eight overarching programme theories were identified featuring mechanisms that were triggered across multiple contexts. Three of these fulfilled a symbolic role in signalling the importance of RCD (e.g. positive role models, signal importance, make a difference), whilst the remainder were more functional (e.g. liberate talents, release resource, exceed sum of parts, learning by doing and co-production of knowledge). Outcomes from one mechanism produced changes in context to stimulate mechanisms in other activities. The eight programme theories were validated with findings from 10 systematic reviews (2014-2017). CONCLUSIONS: This realist synthesis is the starting point for constructing an RCD framework shaped by these programme theories. Future work is required to further test and refine these findings against empirical data from intervention studies

Subsystems, Flowpaths, and the Spatial Variability of Nitrogen in a Fluvial Ecosystem

Nutrient dynamics in rivers affect biogeochemical fluxes from land to oceans and the atmosphere. Fluvial ecosystems are thus important environments for understanding spatial variability in nutrient concentrations. At the San Pedro River in semi-arid Arizona, USA, we investigated how variability in dissolved inorganic nitrogen (DIN) was regulated by subsystem type and hydrological flowpaths. The three subsystems we compared were the riparian zone, parafluvial (gravel bar) zone, and surface stream. DIN concentration was greater in the riparian zone than in the surface stream, suggesting that the riparian zone retains DIN and is a source of N for the surface stream. Parafluvial zones were too variable to generalize how they regulate DIN. Our hypothesis that subsystem type regulates DIN oxidation was too simple. The riparian and parafluvial zones host a mosaic of oxidizing and reducing conditions, as they exhibited highly variable ammonium to nitrate (NH 4 +:NO 3 - ) ratios. Surface stream DIN was dominated by NO 3 - . Along a subsurface flowpath in the riparian zone, we did not observe spatial covariation among the N forms and transformations involved in mineralization. We also compared spatial variability in solute concentrations between flowpaths and non-flowpath reference areas. Our mixed results suggest that spatial variability is regulated in part by flowpaths, but also by solute-specific processing length along a flowpath. To understand the distribution of N in fluvial ecosystems, subsystem type and flowpaths are readily discernable guides, but they should be coupled with other mechanistic factors such as biota and soil type