Networked innovation in the health sector: comparative qualitative study of the role of Collaborations for Leadership in Applied Health Research and Care in translating research into practice
Why this work is in the frame
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Bibliographic record
Abstract
Background Collaborations for Leadership in Applied Health Research and Care (CLAHRCs) were an initiative of the National Institute for Health Research in response to a new research and development strategy in the NHS: ‘Best Research for Best Health’. They were designed to address the ‘second gap in translation’ identified by the Cooksey review; namely, the need to improve health care in the UK by translating clinical research into practice more effectively. Nine CLAHRCs, each encompassing a university in partnership with local NHS bodies, were funded over the period 2008–13. Aims The aim of this report is to provide an independent and theory-based evaluation of CLAHRCs as a new form of networked innovation in the health sector. This evaluation is based on an intensive research study involving three CLAHRCs in the UK and three international organisations (one in the USA and two in Canada). This study was carried out over two overlapping time phases so as to capture changes in the CLAHRCs over time. Networked innovation in the health sector is conceptualised as involving the translation of knowledge via informal social networks. Methods A mix of research methods was used to help ensure the validity and generalisability of the study. These methods addressed the development of each CLAHRC over time, over multiple levels of analysis, and with particular reference to the translation of knowledge across the groups involved, and the quality of the informal underpinning network ties that supported such translation. Research methods, therefore, included a qualitative enquiry based on case studies and case analysis, cognitive mapping methods, and social network analysis. Findings Through our study, we found that each one of our samples of CLAHRCs appropriated the CLAHRC idea in a particular way, depending on their different interpretations or ‘visions’ of the CLAHRC’s role in knowledge translation (KT), and different operating models of how such visions could be achieved. These helped to shape the development of social networks (centralised vs. decentralised) and each CLAHRC’s approach to KT activity (‘bridging’ vs. ‘blurring’ the boundaries between professional groups). Through a comparative analysis, we develop an analytical model of the resultant capabilities which each case, including our international sites, developed for undertaking innovation, encompassing a combination of both ‘integrative capability’ (the ability to move back and forth between scientific evidence and practical application) and ‘relational capability’ (the ability of groups and organisations to work together). This extends previous models of KT by highlighting the effects of leadership and management, and the emergence of social network structures. We further highlight the implications of this analysis for policy and practice by discussing how network structures and boundary-spanning roles and activities can be tailored to different KT objectives. Conclusions Different interpretations and enactments of the CLAHRC mission ultimately led to differing capabilities for KT among our studied initiatives. Further research could usefully explore how these different capabilities are produced, and how they may be more or less appropriate for particular national health-care settings, with a view to improving the design blueprint for future KT initiatives. Funding The National Institute for Health Research Health Services and Delivery Research programme.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.154 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it