Human resource management practices and nursing turnover
Notice bibliographique
Résumé
Background: The objective of this study is to explore the nature of the association of discrete systems of human resource management practices and the rate of voluntary turnover of registered nurses (RNs) in a large sample of Canadian acute care hospitals and nursing homes. Turnover of nursing staff is a significant issue affecting healthcare cost, quality and access. The rate of annual turnover for registered nurses has been estimated to be about 20 percent in Canada, with a total cost of almost US$27,000 for each RN. Healthcare organizations that employ large numbers of registered nurses are keen to learn more about the role that organizational policies and practices play in reducing voluntary turnover, including the role of management and its deployment of human resources. The relationship of human resource management (HRM) practices and employee turnover has generally been under-investigated. Three HRM systems, composed of bundles of discrete workplace and employment practices, were assembled in order to assess the relationship of HRM practice systems with the rate of voluntary RN turnover, at the establishment level. The three HRM practice systems constructed are: a technical HRM system that focuses on policies and practices that regulate the employee-employer relationship; a quality-of-worklife HRM system that includes a variety of employee- and family-friendly employment practices; a high-involvement HRM system that involve workplace arrangements that increase commitment, engagement and participation of employees. Method and analysis: A questionnaire was sent to the chief nursing officers of 2,208 hospitals and long-term care facilities in every province and territory of Canada, yielding valid responses from 705 establishments. Each of the three HRM practice systems is estimated at each facility and comprises eight (8) discrete employment and workplace practices. Using estimates of voluntary RN turnover as the dependent variable, the analysis featured a step-wise ordinary least squares (OLS) regression model for each practice system. Results: After controlinf for facility characteristics and local labour market conditions, the quality-of-worklife HRM employment system ( p <.05) and high-involvement HRM works system ( p <.05) are found to be associated with lower voluntary turnover, yet their overall relationship with facility turnover appears to be quite modest. Our technical HRM system is not found to be associated with facility RN turnover. Conclusion: HRM systems comprising employee-friendly employment practices and nursing high-involvement work practices may play a significant (yet modest) role in reducing RN turnover.
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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 | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».