Wellness in Nursing Education to Promote Resilience and Reduce Burnout: Protocol for a Holistic Multidimensional Wellness Intervention and Longitudinal Research Study Design in Nursing Education
Notice bibliographique
Résumé
BACKGROUND: The United States faces a nursing shortage driven by a burnout epidemic among nurses and nursing students. Nursing students are an integral population to fuel the nursing workforce at high risk of burnout and increased rates of perceived stress. OBJECTIVE: The aim of this paper is to describe WellNurse, a holistic, interdisciplinary, multidimensional longitudinal research study that examines evidence-based interventions intended to reduce burnout and increase resilience among graduate and undergraduate nursing students. METHODS: Graduate and undergraduate nursing students matriculated at a large public university in the northeastern United States are eligible to enroll in this ongoing, longitudinal cohort study beginning in March 2021. Participants complete a battery of health measurements twice each semester during the fourth week and the week before final examinations. The measures include the Perceived Stress Scale, the Satisfaction with Life Scale, the Oldenburg Burnout Inventory, the Brief Resilience Scale, and the Pittsburgh Sleep Quality Index. Participants are eligible to enroll in a variety of interventions, including mindfulness-based stress reduction, mindful eating, fitness training, and massage therapy. Those who enroll in specific, targeted interventions complete additional measures designed to target the aim of the intervention. All participants receive a free Fitbit device. Additional environmental changes are being implemented to further promote a culture that supports academic well-being, including recruiting a diverse student population through evidence-based holistic admissions, inclusive teaching design, targeted resilience and stress reduction workshops, and cultural shifts within classrooms and curricula. The study design protocol is registered at Open Science Framework (DOI 10.17605/OSF.IO/NCBPE). RESULTS: The project was funded on January 1, 2022. Data collection started in March 2022. A total of 267 participants have been recruited. Results will be published after each semester starting in December 2023. WellNurse evaluation follows the Rapid Cycle Quality Improvement framework to continuously monitor ongoing project processes, activity outcomes, and progress toward reducing burnout and increasing resilience. Rapid Cycle Quality Improvement promotes the ability to alter WellNurse interventions, examine multiple interventions, and test their effectiveness among the nursing education population to identify the most effective interventions. CONCLUSIONS: Academic nursing organizations must address student burnout risk and increase resilience to produce a future workforce that provides high-quality patient care to a diverse population. Findings from WellNurse will support evidence-based implementations for public baccalaureate and master's nursing programs in the United States. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49020.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».