Outcome Mapping

{Excerpt} Development is about people—it is about how they relate to one another and their environment, and how they learn in doing so. Outcome mapping puts people and learning first and accepts unexpected change as a source of innovation. It shifts the focus from changes in state, viz. reduced poverty, to changes in behaviors, relationships, actions, and activities. Development agencies must show that their activities make significant and lasting contributions to the welfare of intended beneficiaries. But they may well be trying to measure results that are beyond their reach: the impacts they cite as evidence are often the result of a confluence of events for which they cannot realistically get full credit. Outcome mapping exposes myths about measuring impacts and helps to answer such questions. A project or program that uses the framework and vocabulary of outcome mapping does not claim the achievement of development impacts, nor does it belittle the importance of changes in state. Rather, it focuses on its contributions to outcomes (that may in turn enhance the possibility of development impacts—the relationship is not inevitably a direct one of cause and effect.) More positively, because outcome mapping limits its concerns to those results that fall strictly within a project or program’s sphere of influence, development agencies can become more specific about the actors they target, the changes they expect to see, and the strategies they employ

Developing A Theory of Change

This is a best practice to obtain clarity about what needs to happen to achieve and sustain the changes, or outcomes, that want to be seen (mapping the outcome pathways to success) and to identify who (people or institutions) can influence these outcomes positively or negatively (mapping the activity ecosystem). It sets the framework for identifying impact, intermediary outcome and process indicators

Relationship Between Quantitative MRI Biomarkers and Patient-Reported Outcome Measures After Cartilage Repair Surgery: A Systematic Review.

Background:Treatment of articular cartilage injuries remains a clinical challenge, and the optimal tools to monitor and predict clinical outcomes are unclear. Quantitative magnetic resonance imaging (qMRI) allows for a noninvasive biochemical evaluation of cartilage and may offer advantages in monitoring outcomes after cartilage repair surgery. Hypothesis:qMRI sequences will correlate with early pain and functional measures. Study Design:Systematic review; Level of evidence, 3. Methods:A PubMed search was performed with the following search terms: knee AND (cartilage repair OR cartilage restoration OR cartilage surgery) AND (delayed gadolinium-enhanced MRI OR t1-rho OR T2 mapping OR dgemric OR sodium imaging OR quantitative imaging). Studies were included if correlation data were included on quantitative imaging results and patient outcome scores. Results:Fourteen articles were included in the analysis. Eight studies showed a significant relationship between quantitative cartilage imaging and patient outcome scores, while 6 showed no relationship. T2 mapping was examined in 11 studies, delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) in 4 studies, sodium imaging in 2 studies, glycosaminoglycan chemical exchange saturation transfer (gagCEST) in 1 study, and diffusion-weighted imaging in 1 study. Five studies on T2 mapping showed a correlation between T2 relaxation times and clinical outcome scores. Two dGEMRIC studies found a correlation between T1 relaxation times and clinical outcome scores. Conclusion:Multiple studies on T2 mapping, dGEMRIC, and diffusion-weighted imaging showed significant correlations with patient-reported outcome measures after cartilage repair surgery, although other studies showed no significant relationship. qMRI sequences may offer a noninvasive method to monitor cartilage repair tissue in a clinically meaningful way, but further refinements in imaging protocols and clinical interpretation are necessary to improve utility

Benefits and challenges of applying outcome mapping in an R4D project

The Community-based Fish Culture in Seasonal Floodplains and Irrigation Systems (CBFC) project is a five year research project supported by the Challenge Program on Water and Food (CPWF), with the aim of increasing productivity of seasonally occurring water bodies through aquaculture. The project has been implemented in Bangladesh, Cambodia, China, Mali and Vietnam, where technical and institutional options for community based aquaculture have been tested. The project began in 2005 and was completed in March 2010. There is an increasing demand for researchers to demonstrate the impact of the work within project time frames, yet development is a complex, non-linear process emerging from changes that traditional, managerial approaches to development fail to capture or to understand. Methods to address unanticipated change and increasingly important æsoftÆ outcomes, such as improved governance have not yet been widely tested or adopted. In response to this gap, this paper describes lessons learned during the pilot testing of Outcome Mapping as part of an action research process in Vietnam, and presents an abridged OM methodology for application at the community level.Research, Impact assessment, Livelihoods

Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease

The diagnosis of Alzheimer's disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations. Quantitative electroencephalography (QEEG) measures have been shown to reflect neurodegenerative processes in AD and might qualify as affordable and thereby widely available markers to facilitate the objectivization of AD assessment. Here, we present a novel framework combining Riemannian tangent space mapping and elastic net regression for the development of brain atrophy markers. While most AD QEEG studies are based on small sample sizes and psychological test scores as outcome measures, here we train and test our models using data of one of the largest prospective EEG AD trials ever conducted, including MRI biomarkers of brain atrophy.Comment: Presented at NIPS 2017 Workshop on Machine Learning for Healt

Research that matters: Outcome mapping for linking knowledge to poverty-reduction actions

An 'Outcome Mapping' approach was applied retrospectively to five diverse, highly collaborative research projects aimed at poverty reduction. Designed to help plan for, clarify, and document intended and actual changes in behaviour, actions, and relationships of groups and organisations that directly influence a project's intended beneficiaries, Outcome Mapping enabled us to identify and describe the strategies and actions that played important roles in the innovations achieved. Successful strategies observed included the use of champions, jointly producing high-profile outputs that enhanced the status of local partners, multiple communication strategies, targeting ongoing policy processes, and strong emphases on and investment in capacity building