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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,055 | 0,060 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,012 | 0,018 |
| Communication savante | 0,006 | 0,007 |
| Science ouverte | 0,003 | 0,010 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».