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Enregistrement W4292805870 · doi:10.1016/j.ijnsa.2022.100094

Doctoral education, advanced practice and research: An analysis by nurse leaders from countries within the six WHO regions

2022· article· en· W4292805870 sur OpenAlexaffabout
Mi Ja Kim, Hugh McKenna, Patricia M. Davidson, Helena Leino‐Kilpi, Andrea Baumann, Hester C. Klopper, Naeema Al‐Gasseer, Wipada Kunaviktikul, Suresh K Sharma, Carla Aparecida Arena Ventura, Tae Wha Lee

Notice bibliographique

RevueInternational Journal of Nursing Studies Advances · 2022
Typearticle
Langueen
DomaineHealth Professions
ThématiqueNursing Roles and Practices
Établissements canadiensMcMaster University
Organismes subventionnairesNational Institute of Nursing ResearchNational Health and Medical Research CouncilMedical Research CouncilNational Research Foundation of KoreaConselho Nacional de Desenvolvimento Científico e TecnológicoIndian Council of Medical ResearchNational Institutes of HealthYonsei UniversityCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorUlster UniversityCollege of Nursing, Yonsei UniversityNational Research FoundationNational Research Council of ThailandWorld Health Organization
Mots-clésScholarshipNurse educationInterdependenceNursing researchPolitical scienceOriginalityNursingMedicineMedical educationSociologyQualitative researchSocial science

Résumé

récupéré en direct d'OpenAlex

Doctoral education, advanced practice and research are key elements that have shaped the advancement of nursing. Their impact is augmented when they are integrated and synergistic. To date, no publications have examined these elements holistically or through an international lens. Like a three-legged stool they are inter-reliant and interdependent. Research is integral to doctoral education and influential in informing best practice. This significance and originality of this discussion paper stem from an analysis of these three topics, their history, current status and associated challenges. It is undertaken by renowned leaders in 11 countries within the six World Health Organisation (WHO) regions: South Africa, Egypt, Finland, United Kingdom, Brazil, Canada, United States, India, Thailand, Australia, and the Republic of Korea. The first two authors used a purposive approach to identify nine recognized nurse leaders in each of the six WHO regions. These individuals have presented and published papers on one or more of the three topics. They have led, or currently lead, large strategic organisations in their countries or elsewhere. All these accomplished scholars agreed to collect relevant data and contribute to the analysis as co-authors. Doctoral education has played a pivotal role in advancing nurse scholarship. Many Doctor of Philosophy (PhD) prepared nurses become faculty who go on to educate and guide future nurse researchers. They generate the evidence base for nursing practice, which contributes to improved health outcomes. In this paper, the development of nursing doctoral programmes is examined. Furthermore, PhDs and professional doctorates, including the Doctor of Nursing Practice, are discussed, and trends, challenges and recommendations are presented. The increasing number of advanced practice nurses worldwide contributes to better health outcomes. Nonetheless, this paper shows that the role remains absent or underdeveloped in many countries. Moreover, role ambiguity and role confusion are commonplace and heterogeneity in definitions and titles, and regulatory and legislatorial inconsistencies limit the role's acceptance and adoption. Globally, nursing research studies continue to increase in number and quality, and nurse researchers are becoming partners and leaders in interdisciplinary investigations. Nonetheless, this paper highlights poor investment in nursing research and a lack of reliable data on the number and amount of funding obtained by nurse researchers. The recommendations offered in this paper aim to address the challenges identified. They have significant implications for policy makers, government legislators and nurse leaders.

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)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,484
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0040,001
Communication savante0,0000,002
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
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 Opus0,164
Tête enseignante GPT0,584
Écart entre enseignants0,420 · 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; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

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

En bref

Citations39
Publié2022
Routes d'admission2
Résumé présentoui

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