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Enregistrement W2065384610 · doi:10.1177/1744987111422427

The future of nursing workforce research

2011· article· en· W2065384610 sur OpenAlexaff
Sean P. Clarke

Notice bibliographique

RevueJournal of research in nursing · 2011
Typearticle
Langueen
DomaineHealth Professions
ThématiqueGlobal Health Workforce Issues
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésWorkforceNursingWorkforce planningHealth careStaffingRestructuringPopulationMedicineNursing shortageBusinessPublic relationsPolitical scienceNurse educationEconomic growthEconomicsFinance

Résumé

récupéré en direct d'OpenAlex

Twenty years and two global nurse shortages ago, nursing workforce research was just emerging as a field. However, workforce participation among nurses and interest in nursing careers had swung widely for many decades before (Friss, 1994). Concerns about working conditions for nurses and difficulties in relations between nurses and other members of the healthcare team had long been recognized but were perhaps accelerated by the second wave of feminism beginning in the 1960s (Sullivan, 2002). Workforce research was arguably a response to the practical problems created by staffing shortages in the mid to late 1980s, when many hospitals went through periods where beds were closed or surgeries were cancelled to cope with staffing shortages. In many communities, nursing school enrollments dropped several years earlier when decreased job availability and declines in the perceived attractiveness of nursing careers led to an inability to cover rebounds in demand and normal attrition. By the late 1990s, when workforce research had already established some roots, developments in healthcare systems worldwide, especially deep cuts and restructuring of healthcare systems, were about to trigger yet another shortage several years down the line. A simplified interpretation of demographic trends (an ‘aging of the population’ story) told us that these earlier shortages were only the beginning of deep imbalances between supply and demand. However, the key ‘game changer’ in postmillennial nurse labor markets has been a global financial crisis that has led to delays in service expansion and restrictions in new hiring. For a variety of reasons, many nurses readying themselves for retirement have put off their plans indefinitely and prospects for new graduates have darkened: many hope this is only a short-term trend. Furthermore, smaller, quieter moves reframing the boundaries among the health professions and between groups of nurses (practical nurses and nurse practitioners are but two examples) are occurring across healthcare systems. Thus, while researchers and professional groups in many countries are retaining predictions of shortages in the long term, the aftershocks of the economic crisis, along with changes (often, but not always, cuts) in healthcare driven by demographic and fiscal realities, may well have reset the future of healthcare employment and prospects for nurses for good. Models of service delivery dominated by professional nurses having exclusive or nearexclusive responsibility for direct care in institutional settings are under fire at the same time as the domination of institution-based over community-based healthcare services appears to be reaching an end. So where next? It is time to reassess the purpose of this field. What makes the nurse workforce special? Is it the broad scale and scope of the services nurses provides? The nature of the work and its physical, emotional and intellectual demands? The historically gendered nature of nursing that has influenced politics within the profession and its interaction with groups and forces outside it? If

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)0,032
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Études des sciences et des technologies, Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,727
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0320,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,003
Études des sciences et des technologies0,0020,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,006
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 Opus0,403
Tête enseignante GPT0,638
Écart entre enseignants0,235 · 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'étudeAutre devis
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

Citations5
Publié2011
Routes d'admission1
Résumé présentoui

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