Work engagement supports nurse workforce stability and quality of care: nursing team‐level analysis in psychiatric hospitals
Notice bibliographique
Résumé
Accessible summary Burnout and work engagement are two sides of one coin, two opposite poles related not only to how workers personally experience their jobs but also to how they experience their jobs within the context of work teams/groups. Engaged workers have a lot of energy, are very enthusiastic about their jobs and are absorbed by their work. Nurses’ job performance in hospitals, including psychiatric hospitals, is dependent upon their relationships with physicians and other healthcare workers and their superiors, how they are involved in the decisions about their work and whether or not they are provided with the right resources and adequate support. When nursing teams are able to perform well, nurses tend to be more engaged and satisfied with their jobs and are more willing to stay in their positions. Engaged nursing teams report better quality of patient care in psychiatric hospitals. Abstract Research in healthcare settings reveals important links between work environment factors, burnout and organizational outcomes. Recently, research focuses on work engagement, the opposite (positive) pole from burnout. The current study investigated the relationship of nurse practice environment aspects and work engagement (vigour, dedication and absorption) to job outcomes and nurse‐reported quality of care variables within teams using a multilevel design in psychiatric inpatient settings. Validated survey instruments were used in a cross‐sectional design. Team‐level analyses were performed with staff members ( n = 357) from 32 clinical units in two psychiatric hospitals in B elgium. Favourable nurse practice environment aspects were associated with work engagement dimensions, and in turn work engagement was associated with job satisfaction, intention to stay in the profession and favourable nurse‐reported quality of care variables. The strongest multivariate models suggested that dedication predicted positive job outcomes whereas nurse management predicted perceptions of quality of care. In addition, reports of quality of care by the interdisciplinary team were predicted by dedication, absorption, nurse–physician relations and nurse management. The study findings suggest that differences in vigour, dedication and absorption across teams associated with practice environment characteristics impact nurse job satisfaction, intention to stay and perceptions of quality of care.
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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 | 0,007 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».