What form of collaboration is best for nursing?: qualitative case studies comparing college-university partnerships
Notice bibliographique
Résumé
Collaboration among colleges and universities has been a topic of discussion for decades, with government, consumers and employers voicing support for seamless education, while maintaining the binary system enjoyed in Ontario. Although the literature review provides types of collaborations and elements of successful relationships, there is little published research on the lived experiences of senior faculty and administrators in any Ontario college-university partnerships, specifically, Collaborative Nursing. The purpose of this study is to explore the lived experiences of senior faculty and administrators, the challenges, barriers, and successes, and to establish whether one form of partnership is best for Collaborative Nursing Programs in Ontario. The goal is to elucidate fundamental components of successful relationships; thereby, providing a framework for developing and maintaining future partnerships, regardless of program. Key components of this study include a review of college-university cooperations, types of partnerships agreements, and theoretical concepts explaining successful relationships and predictive problems. The background chapter discusses Ontario's binary system, the evolution of nursing education, and the role of the provincial government in these initiatives. Incorporating a mixed methodology, quantitative data was collected from 17 colleges and 7 universities responding to the survey. Utilizing a naturalistic qualitative approach of inquiry and criteria-based sampling, 30 individuals participated in the four case studies. From the analysis and interpretation of the findings, it was learned that, although college and university professors and administrators were committed to the joint-program and the entry-to-practice requirements, relationship development was neither smooth nor easy. In comparing the four cases to the conceptual framework, it was found that most partnerships did incorporate some success elements, and many struggled with predictive problems in institutional differences, inflexibility, and conflicting external directives. In spite of their difficulties, differences in opinions and approaches, participants supported the concept of partnerships and joint-program delivery. It was discovered that partners who incorporated many of the conceptual elements, had open and honest communication, and diligently worked through differences, reaped successful relationships. Recommendations promote the use of the conceptual framework in developing and sustaining successful partnerships; several support cooperation between colleges and universities in overcoming barriers. Collaboration is a brilliant concept! (PsycInfo Database Record (c) 2020 APA, all rights reserved)
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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 | 0,041 | 0,056 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,007 |
| Études des sciences et des technologies | 0,023 | 0,016 |
| Communication savante | 0,010 | 0,009 |
| Science ouverte | 0,004 | 0,014 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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.
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 ».