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Enregistrement W2091124528 · doi:10.1111/j.1440-1800.2008.00414.x

Leveraging nursing research to transform healthcare systems

2008· editorial· en· W2091124528 sur OpenAlexaff
Nancy Edwards

Notice bibliographique

RevueNursing Inquiry · 2008
Typeeditorial
Langueen
DomaineMedicine
ThématiqueGlobal Health and Surgery
Établissements canadiensUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésNursing shortageNursingNurse educationNursing researchHealth careDeclarationMedicinePolitical scienceEconomic growthEconomics

Résumé

récupéré en direct d'OpenAlex

Calls to strengthen the generation and use of nursing science are not new. Prominent nursing leaders (Antrobus and Kitson 1999; Alcock and Arthur 2002; PAHO 2004), the Munich Declaration (WHO/Europe 2000), and the World Health Assembly (2006) have emphasized the need for nursing research to inform healthcare decision-making. Despite repeated calls for action, progress has been slow. And now, we find ourselves at a critical junction; the encroaching worldwide nursing shortage has the potential to undermine the gains already made. This threat is particularly worrisome in lower-income countries where existing nursing research capacity is extremely fragile and the nursing shortage is most acute. In sub-Saharan Africa, for example, it is estimated that another 600 000 nurses are needed to meet the Millennium Development Goals (Buchan and Calman 2004). In the Caribbean, the outflow of migrating nurses to the UK and to North American destinations is likely to worsen as international recruitment efforts build. The purpose of this editorial is to consider the current state of nursing research and discuss how we might maintain momentum through the pending challenges of nursing shortages. Many have worked to establish research as a critical nursing function. Increasingly, in both baccalaureate and diploma programmes around the world, nursing students are introduced to the basics of research as a foundation of their education. Research capacity has grown with the establishment of graduate programmes for nurses, and efforts to increase the financial and geographical accessibility of these programmes. Although these initiatives remain concentrated in higher-income countries, colleagues in many lower- and middle-income countries are leading important collaboratives to increase the accessibility of graduate education opportunities and to fast-track research preparation for nurses (Galvin and Courtney 2005). The essential infrastructure required to support nursing research is also growing, albeit slowly. Worldwide, obtaining research funding remains a highly competitive process that is positioned within a decision-making structure that is predominantly biomedical in its orientation (Rafferty and Traynor 2002). However, nurse scientists are demonstrating success, querying eligibility restrictions on health research funds that preclude the submission of nursing research protocols, and assembling strong teams including experienced researchers who are successfully putting forward novel and competitive projects. Gradually, nursing research is gaining entrée to decision-makers. Cumulative bodies of evidence are demonstrating the cost-effectiveness of nursing services and beginning to fuel important debates regarding healthcare investments. Although the topography of nursing research capacity, access and use is very uneven, overall, nursing research now has a foothold in the realms of decision-makers. But as we face the nursing shortage, how do we mitigate the risk that nursing research may be seen as a luxury, rather than as an essential element to inform the provision of effective and accessible healthcare services? I suggest that several strategies are needed. First, nursing research must be clearly positioned within national and international science and technology strategies. We need clear statements of the essential role of nursing science in addressing our most pressing health issues including the growing disparities between rich and poor. Second, we must create channels to communicate to decision-makers, promising and highly pertinent examples of nursing innovations that are the product of research. Researchers must pay particular attention to the potential scaling-up of new service delivery models with demonstrated effectiveness. Third, we must be prepared to support and nurture programmatic research. We need larger-scale programmes of research that build in mentorship and knowledge translation, and bridge the work of health services, clinical and population health research. The results of programmatic research are essential to inform investment decisions. We must move beyond nursing research that predominantly involves small-scale studies. Although these are often useful for local quality improvement strategies, they are rarely externally generalizable or eligible for inclusion within systematic or integrative reviews of evidence. So how do we get there? There need to be concerted efforts by leaders in nursing service, policy and research communities to provide illustrative examples of how nursing research can make a difference. We must be prepared to discuss how the products of nursing research can bring a return on investment and address the emerging challenges of nursing shortages. We need to assemble context-relevant sets of case studies illustrating the indirect and direct impact of nursing research in various parts of the world. We need prototypes of programmes in nursing research to bring to the decision-making table. It is imperative that we articulate nursing research questions that tackle clinical, service delivery and policy issues. We must remember that the world of nursing research is opaque to most. It is a world where most biomedical scientists have never ventured or been invited to explore. It is up to nurse scientists and nursing leaders to change this reality. We must engage top-level research scientists, from diverse fields, as co-mentors on our teams. This would bring the advantages of strong mentorship alongside learning opportunities for senior scientists to explore the arena of nursing research. And we must apply the most innovative and effective knowledge translation strategies to undertake the complex business of transforming nursing research evidence into practice and policy. Our future successes will require the confluence of national and global strategic visions that include building nurses’ capacity to do and use research, providing essential infrastructure to support nursing research, and orchestrating knowledge translation strategies that use nursing science as a critical input for health systems transformation. We have come too far to lose our research foothold now.

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)0,003
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-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 enseignants0,052
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,001
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0020,004
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,139
Tête enseignante GPT0,461
Écart entre enseignants0,322 · 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

Citations6
Publié2008
Routes d'admission1
Résumé présentoui

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