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Enregistrement W4394853659 · doi:10.11124/jbies-24-00073

Unlocking the power of global collaboration: building a stronger evidence ecosystem together

2024· editorial· en· W4394853659 sur OpenAlexaff
Zoe Jordan, Vivian Welch, Karla Soares‐Weiser

Notice bibliographique

RevueJBI Evidence Synthesis · 2024
Typeeditorial
Langueen
DomaineDecision Sciences
ThématiqueEvaluation and Performance Assessment
Établissements canadiensCampbell Scientific (Canada)
Organismes subventionnairesnon disponible
Mots-clésEcosystemPower (physics)Environmental resource managementBusinessEnvironmental scienceEcologyBiologyPhysics

Résumé

récupéré en direct d'OpenAlex

Across the global evidence ecosystem, numerous organizations share a common vision and mission: to promote evidence-based decision-making worldwide. These organizations, including JBI, the Cochrane Collaboration, and the Campbell Collaboration, have each made an indelible imprint on the evidence-based movement and have been identified as “a crucial mechanism to facilitate the synthesis, transfer, and implementation of evidence into health care policy and practice.”1(p.211) While the benefits of global collaboration have been well established for some time, achieving impact at scale will require a fundamental shift in mindset. The COVID-19 pandemic marked a turning point for evidence-based health care and decision-making. It provided a unique context whereby policymakers, health care providers, researchers, and the public required immediate access to trustworthy evidence to make decisions. We collectively faced major challenges in translating a rapidly evolving body of new evidence into tangible response efforts, with health policy decisions receiving unprecedented public attention. The “stress test” of COVID-19, and the many post-pandemic initiatives that followed, highlighted the need for more effective strategies, institutional mechanisms, and capacities to systematically mobilize and contextualize the best available evidence for rapid decision-making for effective and equitable public health responses.2–4 Each of our organizations responded to COVID-19 in different ways and were able to provide access to reliable evidence. Yet, it is essential to acknowledge the challenges of sustaining funding, upholding methodological rigor, and ensuring diversity and inclusivity in our collective endeavors. Our demonstrated success in enhancing global health care, education, and social policy underscores the value of collaborative, evidence-based approaches in addressing the world’s most pressing challenges. We find ourselves at a unique juncture where our respective global collaborative evidence networks (JBI, Cochrane, and Campbell) must reimagine the way we work together to facilitate and engage in multidisciplinary, transdisciplinary, and interdisciplinary research, dissemination, knowledge sharing, and knowledge translation to generate impact at scale across the evidence ecosystem. It is time to develop interagency collaboration as a coherent program rather than a series of standalone efforts. There is significant potential in our ability to orchestrate, integrate, coordinate, and align our activities to identify opportunities for mutual benefit, learning, and impact. A call to action One of the most significant benefits of our respective global networks is our capacity to transcend geographic boundaries. By facilitating better global interagency collaboration, we enable the pooling of expertise and knowledge in the field of evidence-based practice, and the result is a more holistic and nuanced understanding of complex issues, leading to improved decision-making at both local and global levels. Examples of this may include much deeper collaboration on methodologies and standards for synthesis that reflect the diversity of evidence to respond to global challenges; a more coordinated approach to the prioritization of synthesis efforts to avoid duplication of effort; and better, more meaningful partnership on the contextualization or localization of evidence for policy and practice. Going forward, it is incumbent upon us to support and strengthen our networks and the relationships between them, recognizing the invaluable contributions we can collectively make to improving the well-being of individuals and communities around the world. The path to a brighter, more evidence-based future lies in continued collaboration and our unwavering commitment to the delivery of trustworthy evidence. Collaboration across our global networks, not just within them, is now not merely a choice but a necessity in our increasingly interconnected world.

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)0,022
score de la tête « metaresearch » (Gemma)0,076
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Communication savante, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,758
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0220,076
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,003
Études des sciences et des technologies0,0000,000
Communication savante0,0020,002
Science ouverte0,0030,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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 Opus0,067
Tête enseignante GPT0,460
Écart entre enseignants0,394 · 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

Citations3
Publié2024
Routes d'admission1
Résumé présentoui

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