Minding the gap: The importance of active facilitation in moving boundary objects from in-theory to in-use as a tool for knowledge mobilisation

The Health Inequalities Assessment Toolkit (HIAT) was developed to support those involved in health research to integrate a focus on health inequalities. Our study focuses on bringing together the concepts of boundary objects (BO) and brokers-as-bricoleurs to explain the implementation of the HIAT within a research capacity building programme. Exploring the extent to which (i) the HIAT operated as a BO and (ii) the ideal conditions to nurture and enhance its effectiveness during knowledge mobilisation. We employed a qualitative approach to analyse: semi-structured focus groups and telephone interviews; secondary data from an evaluation of the wider research programme within which the capacity building was situated. Data was thematically analysed incorporating the properties of a BO: meaningfulness, convergence, resonance and authenticity. Four main themes identified: (1) Generating convergence through creating a focus (2) Reconciling differences to create a common language (3) Workshop facilitators: boundary brokers-as-bricoleurs, (4) Thoughts into action. The HIAT operated as a BO, enabling individuals across the different project teams to galvanise around the issue of health inequalities, explore collaboratively and incorporate equity within service evaluations. Highlighting the importance of involving brokers with an ability to improvise and mobilise around the HIAT, using their expertise to translate and interpret across boundaries and emphasise shared goals. Reflecting on this, a modified tool with additional resources beyond socio-economic causes has been launched as a forum to consider health inequalities from diverse perspectives for use beyond UK health and social care research

Implementing health research through academic and clinical partnerships : a realistic evaluation of the Collaborations for Leadership in Applied Health Research and Care (CLAHRC)

Background: The English National Health Service has made a major investment in nine partnerships between higher education institutions and local health services called Collaborations for Leadership in Applied Health Research and Care (CLAHRC). They have been funded to increase capacity and capability to produce and implement research through sustained interactions between academics and health services. CLAHRCs provide a natural ‘test bed’ for exploring questions about research implementation within a partnership model of delivery. This protocol describes an externally funded evaluation that focuses on implementation mechanisms and processes within three CLAHRCs. It seeks to uncover what works, for whom, how, and in what circumstances. Design and methods: This study is a longitudinal three-phase, multi-method realistic evaluation, which deliberately aims to explore the boundaries around knowledge use in context. The evaluation funder wishes to see it conducted for the process of learning, not for judging performance. The study is underpinned by a conceptual framework that combines the Promoting Action on Research Implementation in Health Services and Knowledge to Action frameworks to reflect the complexities of implementation. Three participating CLARHCS will provide indepth comparative case studies of research implementation using multiple data collection methods including interviews, observation, documents, and publicly available data to test and refine hypotheses over four rounds of data collection. We will test the wider applicability of emerging findings with a wider community using an interpretative forum. Discussion: The idea that collaboration between academics and services might lead to more applicable health research that is actually used in practice is theoretically and intuitively appealing; however the evidence for it is limited. Our evaluation is designed to capture the processes and impacts of collaborative approaches for implementing research, and therefore should contribute to the evidence base about an increasingly popular (e.g., Mode two, integrated knowledge transfer, interactive research), but poorly understood approach to knowledge translation. Additionally we hope to develop approaches for evaluating implementation processes and impacts particularly with respect to integrated stakeholder involvement

Beyond the limits of clinical governance? The case of mental health in English primary care

Background: Little research attention has been given to attempts to implement organisational initiatives to improve quality of care for mental health care, where there is a high level of indeterminacy and clinical judgements are often contestable. This paper explores recent efforts made at an organisational level in England to improve the quality of primary care for people with mental health problems through the new institutional processes of 'clinical governance'. Methods: Framework analysis, based on the Normalisation Process Model (NPM), of attempts over a five year period to develop clinical governance for primary mental health services in Primary Care Trusts (PCTs). The data come from a longitudinal qualitative multiple case-study approach in a purposive sample of 12 PCTs, chosen to reflect a maximum variety of organisational contexts for mental health care provision. Results: The constant change within the English NHS provided a difficult context in which to attempt to implement 'clinical governance' or, indeed, to reconstruct primary mental health care. In the absence of clear evidence or direct guidance about what 'primary mental health care' should be, and a lack of actors with the power or skills to set about realising it, the actors in 'clinical governance' had little shared knowledge or understanding of their role in improving the quality of mental health care. There was a lack of ownership of 'mental health' as an integral, normalised part of primary care. Conclusion: Despite some achievements in regard to monitoring and standardisation of prescribing practice, mental health care in primary care seems to have so far largely eluded the gaze of 'clinical governance'. Clinical governance in English primary mental health care has not yet become normalised. We make some policy recommendations which we consider would assist in the process normalisation and suggest other contexts to which our findings might apply

Depression and sickness behavior are Janus-faced responses to shared inflammatory pathways

It is of considerable translational importance whether depression is a form or a consequence of sickness behavior. Sickness behavior is a behavioral complex induced by infections and immune trauma and mediated by pro-inflammatory cytokines. It is an adaptive response that enhances recovery by conserving energy to combat acute inflammation. There are considerable phenomenological similarities between sickness behavior and depression, for example, behavioral inhibition, anorexia and weight loss, and melancholic (anhedonia), physio-somatic (fatigue, hyperalgesia, malaise), anxiety and neurocognitive symptoms. In clinical depression, however, a transition occurs to sensitization of immuno-inflammatory pathways, progressive damage by oxidative and nitrosative stress to lipids, proteins, and DNA, and autoimmune responses directed against self-epitopes. The latter mechanisms are the substrate of a neuroprogressive process, whereby multiple depressive episodes cause neural tissue damage and consequent functional and cognitive sequelae. Thus, shared immuno-inflammatory pathways underpin the physiology of sickness behavior and the pathophysiology of clinical depression explaining their partially overlapping phenomenology. Inflammation may provoke a Janus-faced response with a good, acute side, generating protective inflammation through sickness behavior and a bad, chronic side, for example, clinical depression, a lifelong disorder with positive feedback loops between (neuro)inflammation and (neuro)degenerative processes following less well defined triggers

The NIRS Analysis Package: Noise Reduction and Statistical Inference

Near infrared spectroscopy (NIRS) is a non-invasive optical imaging technique that can be used to measure cortical hemodynamic responses to specific stimuli or tasks. While analyses of NIRS data are normally adapted from established fMRI techniques, there are nevertheless substantial differences between the two modalities. Here, we investigate the impact of NIRS-specific noise; e.g., systemic (physiological), motion-related artifacts, and serial autocorrelations, upon the validity of statistical inference within the framework of the general linear model. We present a comprehensive framework for noise reduction and statistical inference, which is custom-tailored to the noise characteristics of NIRS. These methods have been implemented in a public domain Matlab toolbox, the NIRS Analysis Package (NAP). Finally, we validate NAP using both simulated and actual data, showing marked improvement in the detection power and reliability of NIRS

Embracing complexity and uncertainty to create impact: Exploring the processes and transformative potential of co-produced research through development of a social impact model

© 2018 The Author(s). The potential use, influence and impact of health research is seldom fully realised. This stubborn problem has caused burgeoning global interest in research aiming to address the implementation 'gap' and factors inhibiting the uptake of scientific evidence. Scholars and practitioners have questioned the nature of evidence used and required for healthcare, highlighting the complex ways in which knowledge is formed, shared and modified in practice and policy. This has led to rapid expansion, expertise and innovation in the field of knowledge mobilisation and funding for experimentation into the effectiveness of different knowledge mobilisation models. One approach gaining prominence involves stakeholders (e.g. researchers, practitioners, service users, policy-makers, managers and carers) in the co-production, and application, of knowledge for practice, policy and research (frequently termed integrated knowledge translation in Canada). Its popularity stems largely from its potential to address dilemmas inherent in the implementation of knowledge generated using more reductionist methods. However, despite increasing recognition, demands for co-produced research to illustrate its worth are becoming pressing while the means to do so remain challenging. This is due not only to the diversity of approaches to co-production and their application, but also to the ways through which different stakeholders conceptualise, measure, reward and use research. While research co-production can lead to demonstrable benefits such as policy or practice change, it may also have more diffuse and subtle impact on relationships, knowledge sharing, and in engendering culture shifts and research capacity-building. These relatively intangible outcomes are harder to measure and require new emphases and tools. This opinion paper uses six Canadian and United Kingdom case studies to explore the principles and practice of co-production and illustrate how it can influence interactions between research, policy and practice, and benefit diverse stakeholders. In doing so, we identify a continuum of co-production processes. We propose and illustrate the use of a new 'social model of impact' and framework to capture multi-layered and potentially transformative impacts of co-produced research. We make recommendations for future directions in research co-production and impact measurement

Back pain outcomes in primary care following a practice improvement intervention:- a prospective cohort study

<p>Abstract</p> <p>Background</p> <p>Back pain is one of the UK's costliest and least understood health problems, whose prevalence still seems to be increasing. Educational interventions for general practitioners on back pain appear to have had little impact on practice, but these did not include quality improvement learning, involve patients in the learning, record costs or document practice activities as well as patient outcomes.</p> <p>Methods</p> <p>We assessed the outcome of providing information about quality improvement techniques and evidence-based practice for back pain using the Clinical Value Compass. This included clinical outcomes (Roland and Morris Disability Questionnaire), functional outcomes, costs of care and patient satisfaction. We provided workshops which used an action learning approach and collected before and after data on routine practice activity from practice electronic databases. In parallel, we studied outcomes in a separate cohort of patients with acute and sub-acute non-specific back pain recruited from the same practices over the same time period. Patient data were analysed as a prospective, split-cohort study with assessments at baseline and eight weeks following the first consultation.</p> <p>Results</p> <p>Data for 1014 patients were recorded in the practice database study, and 101 patients in the prospective cohort study. We found that practice activities, costs and patient outcomes changed little after the intervention. However, the intervention was associated with a small, but statistically significant reduction in disability in female patients. Additionally, baseline disability, downheartedness, self-rated health and leg pain had small but statistically significant effects (p < 0.05) on follow-up disability scores in some subgroups.</p> <p>Conclusions</p> <p>GP education for back pain that both includes health improvement methodologies and involves patients may yield additional benefits for some patients without large changes in patterns of practice activity. The effects in this study were small and limited and the reasons for them remain obscure. However, such is the impact of back pain and its frequency of consultation in general practice that this kind of improvement methodology deserves further consideration.</p> <p>Trial registration number</p> <p>ISRCTN: <a href="http://www.controlled-trials.com/ISRCTN30420389">ISRCTN30420389</a></p

Denoising Two-Photon Calcium Imaging Data

Two-photon calcium imaging is now an important tool for in vivo imaging of biological systems. By enabling neuronal population imaging with subcellular resolution, this modality offers an approach for gaining a fundamental understanding of brain anatomy and physiology. Proper analysis of calcium imaging data requires denoising, that is separating the signal from complex physiological noise. To analyze two-photon brain imaging data, we present a signal plus colored noise model in which the signal is represented as harmonic regression and the correlated noise is represented as an order autoregressive process. We provide an efficient cyclic descent algorithm to compute approximate maximum likelihood parameter estimates by combing a weighted least-squares procedure with the Burg algorithm. We use Akaike information criterion to guide selection of the harmonic regression and the autoregressive model orders. Our flexible yet parsimonious modeling approach reliably separates stimulus-evoked fluorescence response from background activity and noise, assesses goodness of fit, and estimates confidence intervals and signal-to-noise ratio. This refined separation leads to appreciably enhanced image contrast for individual cells including clear delineation of subcellular details and network activity. The application of our approach to in vivo imaging data recorded in the ferret primary visual cortex demonstrates that our method yields substantially denoised signal estimates. We also provide a general Volterra series framework for deriving this and other signal plus correlated noise models for imaging. This approach to analyzing two-photon calcium imaging data may be readily adapted to other computational biology problems which apply correlated noise models.National Institutes of Health (U.S.) (DP1 OD003646-01)National Institutes of Health (U.S.) (R01EB006385-01)National Institutes of Health (U.S.) (EY07023)National Institutes of Health (U.S.) (EY017098

Evidence in the learning organization

<p>Abstract</p> <p>Background</p> <p>Organizational leaders in business and medicine have been experiencing a similar dilemma: how to ensure that their organizational members are adopting work innovations in a timely fashion. Organizational leaders in healthcare have attempted to resolve this dilemma by offering specific solutions, such as evidence-based medicine (EBM), but organizations are still not systematically adopting evidence-based practice innovations as rapidly as expected by policy-makers (the knowing-doing gap problem). Some business leaders have adopted a systems-based perspective, called the learning organization (LO), to address a similar dilemma. Three years ago, the Society of General Internal Medicine's Evidence-based Medicine Task Force began an inquiry to integrate the EBM and LO concepts into one model to address the knowing-doing gap problem.</p> <p>Methods</p> <p>During the model development process, the authors searched several databases for relevant LO frameworks and their related concepts by using a broad search strategy. To identify the key LO frameworks and consolidate them into one model, the authors used consensus-based decision-making and a narrative thematic synthesis guided by several qualitative criteria. The authors subjected the model to external, independent review and improved upon its design with this feedback.</p> <p>Results</p> <p>The authors found seven LO frameworks particularly relevant to evidence-based practice innovations in organizations. The authors describe their interpretations of these frameworks for healthcare organizations, the process they used to integrate the LO frameworks with EBM principles, and the resulting Evidence in the Learning Organization (ELO) model. They also provide a health organization scenario to illustrate ELO concepts in application.</p> <p>Conclusion</p> <p>The authors intend, by sharing the LO frameworks and the ELO model, to help organizations identify their capacities to learn and share knowledge about evidence-based practice innovations. The ELO model will need further validation and improvement through its use in organizational settings and applied health services research.</p

Psychometric evaluation of a short measure of social capital at work

BACKGROUND: Prior studies on social capital and health have assessed social capital in residential neighbourhoods and communities, but the question whether the concept should also be applicable in workplaces has been raised. The present study reports on the psychometric properties of an 8-item measure of social capital at work. METHODS: Data were derived from the Finnish Public Sector Study (N = 48,592) collected in 2000–2002. Based on face validity, an expert unfamiliar with the data selected 8 questionnaire items from the available items for a scale of social capital. Reliability analysis included tests of internal consistency, item-total correlations, and within-unit (interrater) agreement by r(wg )index. The associations with theoretically related and unrelated constructs were examined to assess convergent and divergent validity (construct validity). Criterion-related validity was explored with respect to self-rated health using multilevel logistic regression models. The effects of individual level and work unit level social capital were modelled on self-rated health. RESULTS: The internal consistency of the scale was good (Cronbach's alpha = 0.88). The r(wg )index was 0.88, which indicates a significant within-unit agreement. The scale was associated with, but not redundant to, conceptually close constructs such as procedural justice, job control, and effort-reward imbalance. Its associations with conceptually more distant concepts, such as trait anxiety and magnitude of change in work, were weaker. In multilevel models, significantly elevated age adjusted odds ratios (ORs) of poor self-rated health (OR = 2.42, 95% confidence interval (CI): 2.24–2.61 for the women and OR = 2.99, 95% CI: 2.56–3.50 for the men) were observed for the employees in the lowest vs. highest quartile of individual level social capital. In addition, low social capital at the work unit level was associated with a higher likelihood of poor self-rated health. CONCLUSION: Psychometric techniques show our 8-item measure of social capital to be a valid tool reflecting the construct and displaying the postulated links with other variables