The relationships between nurses’ work environments and emotional exhaustion, job satisfaction, and intent to leave among nurses in Saudi Arabia
Notice bibliographique
Résumé
AIMS: To examine relationships between components of nurses' work environments and emotional exhaustion, job satisfaction and intent to leave among nurses in Saudi Arabia. DESIGN: A descriptive correlational study with cross-sectional data. METHODS: Data were collected in 2017 from 497 Registered Nurses working in a large tertiary hospital in Riyadh, Saudi Arabia. Participants completed an online survey like that used in RN4Cast studies to measure nurses' perceptions of their work environments and nurse outcomes. Hierarchical linear regression and logistic regression were conducted to examine the relationships between components of nurses' work environments and nurse outcomes after controlling for nurse and patient characteristics. RESULTS: Nurse participation in hospital affairs was uniquely associated with all three nurse outcomes, whereas staffing and resource adequacy was associated with emotional exhaustion and job satisfaction, but not intent to leave. These two variables were also the components of the nursing practice environment that received the lowest ratings. Nurse manager ability, leadership and support of nurses, and nurse-physician relationships were associated with job satisfaction only. A nursing foundation for quality of care was not uniquely associated with any of the three outcomes. Finally, nurse emotional exhaustion and job satisfaction fully mediated the relationship between nurse participation in hospital affairs and intent to leave. CONCLUSION: Magnet-like work environments in Saudi Arabia are critical to recruiting and retaining nurses in a country with critical nursing shortages. IMPACT: This study addresses a gap in the literature regarding which components of the nurses' work environment are uniquely associated with emotional exhaustion, job satisfaction and intent to leave among nurses in Saudi Arabia. Study results will assist Saudi hospital administrators and nurse leaders to develop recruitment and retention strategies by focusing on those work environment components most associated with nurse outcomes: participation in hospital affairs and staffing and resource adequacy.
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 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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».