Notice bibliographique
Résumé
The attention to improve global health has developed significantly in the last decade through the introduction of dedicated scientific journals such as the International Journal of Evidence-Based Health Care, BMJ Quality and Safety and others. Furthermore, the ongoing involvement of major research funding bodies such as the National Health and Research Council, the Australian Research Council, the Canadian Institute of Health and Research and the National Health Institute in the United States and other organizations around the world have helped to fund important research projects to achieve equality in healthcare in their respective countries.1 Implementation of research findings across healthcare services will improve global health. This can be achieved through a scientific approach to understand how knowledge is translated into healthcare practice, management and policy to achieve the best health outcomes globally. As clinicians and researchers are engaged in providing and improving healthcare through different measures, many focus on targeting specific health profession groups, healthcare organizations or specialized clinical areas. A narrow targeted strategy may limit our progression in improving healthcare globally. The diversity of our disciplines in research, education and practice should enrich and strengthen our efforts to improve healthcare globally through engaging experts from a variety of disciplines, including behavioural economics, management science and systems engineering, to develop new models of care. We seek improved healthcare that effectively and equitably serve all people globally.2 Recognizing the culture differences between populations and achieving culture competency is also essential in reducing the health disparities experienced by many individuals worldwide. Evidence suggests that both health professionals’ leadership and diversity in institutional training are core areas to be targeted to improve healthcare globally. Leadership must be cohesive and supportive at executive levels and diffused throughout the various layers of the organizations. Diversity training must be embedded in programs across healthcare settings and should aim to raise awareness of the different health needs of patients.3 Creating international links between researchers and clinicians is essential in enhancing scientific discussion and creating a debate on how to establish a shared vision between stakeholders to provide scientific knowledge on how to improve healthcare globally.4 Other considerations for a successful research agenda to improve global health include: a defined target audience, identification of key research areas, context, understanding behavioural determinants, an implementation strategy and evaluation of change strategies, testing theories and other issues such as sustainability, knowledge infrastructure and workforce.5 The 9th Biennial 2014 Joanna Briggs Colloquium theme ‘Scaling new heights’ provides an international forum to discuss and challenge several beliefs around global health. The Colloquium focuses on improving healthcare globally through exploring some contemporary issues including; e-health as an innovative method for expanding evidence based practice and its impact on global health, revitalizing the fundamentals of care in the 21st Century, shared decision making and patients’ engagement in chronic disease management.
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 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 | 0,015 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,006 | 0,021 |
| Communication savante | 0,016 | 0,033 |
| Science ouverte | 0,002 | 0,026 |
| Intégrité de la recherche | 0,009 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,061 | 0,008 |
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 ».