Facility Turnover and Vacancy Rates of Registered Nurses: Do They Predict How Nurses Are Recruited?
Notice bibliographique
Résumé
Objectives: Healthcare organisations in Western industrialised countries are experiencing nursing labour markets characterised by extreme staff shortages and high levels of turnover and vacancy for Registered Nurses (RNs). The effective recruitment and retention of nursing personnel are considered an essential management function if healthcare organisations wish to survive and prosper in these difficult times. The objective of this study is to examine the relationship between healthcare establishment turnover and vacancy rates of RNs and the means these establishments use to recruit nursing personnel. It is predicted that in the face of higher turnover and vacancy rates for registered nurses, healthcare organisations will utilise more active (employer-initiated) and fewer passive (employee-initiated) recruitment channels. Method: Data for this study were collected from over 700 hospital and nursing homes in Canada. Directors of Nursing at these establishments were asked about the use of various recruitment channels to attract nursing personnel. Results: Bi- and multi-variate analyses were performed to characterise the relationships between establishment RN turnover and vacancy rates with respect to the selection of recruitment channel utilised. Ordinary Least Square regression analysis showed that perceived vacancy rate, and to a lesser degree turnover of RNs, were strong predictors of the use of more active recruitment channels. Healthcare organisations with a local labour market characterised by a greater supply of employable RNs, were more likely to use more passive channels, even in the face of higher RN turnover and vacancy. Healthcare organisations which were perceived as being stronger 'employers-of-choice,' were also more likely to use more passive recruitment channels, even in the face of higher vacancies for RNs. Conclusion: Results from this study suggest that when labour markets have a larger surplus of RNs for potential employment, establishments are less than proactive in their attempts to vigorously recruit. During these times, having a perception of being a strong employerof- choice, enables healthcare organisations to maintain full employment without having to launch aggressive recruitment initiatives.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».