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Enregistrement W2778519293 · doi:10.1097/xeb.0000000000000133

Leveraging expertise

2017· editorial· en· W2778519293 sur OpenAlexaboutno aff
Zoe Jordan

Notice bibliographique

RevueInternational Journal of Evidence-Based Healthcare · 2017
Typeeditorial
Langueen
DomaineHealth Professions
ThématiquePrimary Care and Health Outcomes
Établissements canadiensnon disponible
Organismes subventionnairesUniversity of Adelaide
Mots-clésHealth careContext (archaeology)Leverage (statistics)Evidence-based practiceBest practicePublic relationsBusinessPsychologyPolitical scienceMedicineManagementEconomicsComputer scienceAlternative medicine

Résumé

récupéré en direct d'OpenAlex

It is now well recognized that, despite considerable investment in the generation of research, for the most part it is not routinely used in practice or policy. The inimitable context of healthcare practice is certainly a contributing factor as it continues to increase in uncertainty, instability and complexity.1 However, with a small investment in building partnerships, there is the potential to achieve significant impacts at the point of care. To achieve delivery of healthcare that is based on the best available evidence, we cannot work in isolation and academic–clinical partnerships offer opportunities to leverage expertise in both sectors to reach this goal. The Joanna Briggs Institute (JBI) Model of Evidence Based Healthcare recognizes that theory and practice are equally important parts of the same agenda and for evidence-based decision-making to occur in an authentic manner it is critical that the evidence is clinically relevant.2 On the contrary, academic and clinical settings do not always align as they could or should and shifting priorities, funding sources and organizational structures can make the creation of meaningful partnerships challenging. Implementation of a formalized framework around clinical partnerships to overcome fundamental differences in academic and health service culture and orientation around evidence-based healthcare could be beneficial in overcoming these challenges. In a similar vein to the Canadian National Institutes of Health practice-based research networks, these partnerships would offer a two-way connection ‘between the interstates of academic scientific discoveries and the patients receiving care in the ambulatory practice’.3 Gaglioti et al.4 believe that these partnerships offer an opportunity to ‘synergize around shared values and goals, and eventually serve as a bridge connecting those working on issues from the towers of policy or academia to those with “boots on the ground” experience’. Similarly, in the United Kingdom, the Collaboration for Leadership in Applied Health Research and Care (CLAHRC) program, funded through the National Institute for Health Research, seeks to address the problem of translation from research-based evidence to routine healthcare practice through partnership.5 Indeed, the CLAHRC program has also initiated an Evidence Synthesis Collaboration to facilitate the use of evidence syntheses by CLAHRC partners by ensuring the syntheses projects are driven by user needs. An Evidence for Change pilot was undertaken in 2015 that sought to encourage the use of the best available evidence to inform professional practice change. The JBI takes the view that a synergistic approach to this endeavor will result in a valuable and powerful approach to addressing translational gaps and achieving an evidence-based approach to the healthcare delivery. For this to occur, the knowledge needs of expert clinicians need to be paired with the skills of experienced academics to synthesize and produce practical, relevant, usable evidence. If evidence-based healthcare is approached as an organizational, collaborative initiative, from synthesis to transfer and implementation, then the potential for increased sustainability and improved health outcomes is surely increased. The international Joanna Briggs Collaboration consists of JBI Centres of Excellence and Affiliated Groups that contribute to furthering the vision and mission of the institute globally through the delivery of high-quality programs of evidence synthesis, transfer and implementation. The academics in these entities agree that health professionals on the frontline of service provision have unique insights to offer the evidence-based endeavor and that academic–clinical partnerships will form a strong foundation for the planning and delivery of evidence-based services. In partnering with a JBI Collaborating Entity, there is significant potential for capacity building of stakeholders and to enhance the learning culture of both settings. Kitson et al.6 remind us of the complexity of knowledge translation and encourage us to think about the role of actors (stakeholders), relationships and networks to actively mobilize knowledge between those involved and to embrace collaborative processes of knowledge production and use. Knowledge translation is not a perfect science, far from it. However, it can surely only be strengthened by finding the synergies across policy, practice and research. Acknowledgements Conflicts of interest The author reports no conflicts of interest.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,016
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Intégrité de la recherche
Catégories consensuellesIntégrité de la recherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,118
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,016
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0030,000
Intégrité de la recherche0,0020,006
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,308
Tête enseignante GPT0,545
Écart entre enseignants0,238 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2017
Routes d'admission1
Résumé présentoui

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