Examining Predictors of Intention to Leave in Home Care and Differences among Types of Providers
Notice bibliographique
Résumé
The retention and recruitment of care providers are ongoing concerns in healthcare globally. Examining intention to leave (ITL) as a measure of retention, the existing literature has focused on nurses working in hospitals, with less attention paid to other care providers and other areas of practice. The purpose of this study was to gain a better understanding of the unique factors influencing ITL among three categories of care providers in home care: registered practical nurses, registered nurses, and personal support workers. This study assessed and compared predictors of ITL, including organizational commitment, job satisfaction, perceived supervisor support, burnout, role stress, work/family conflict, and community satisfaction. A convenience sample of home care staff working in one agency in a Canadian province was sent an electronic survey by e-mail in 2021. Responses (n = 185) underwent data analysis including descriptive statistics, analysis of variance, and multiple linear regression, as well as thematic analysis of two open-ended items. The results of the study indicated that 54% (n = 99) of respondents were considering leaving their job, and respondents were dissatisfied with their salary and benefits. Role stress, work-family conflict, and burnout differed significantly between groups. Several themes emerged for strategies to promote employees to stay with the agency, with the overwhelming strategy being higher wages/salary. Themes for why employees stayed with the agency included love for clients and commitment to their care, as well as fondness for the teams within which respondents worked. The findings of the study lead to several important implications and recommendations for the home care sector. Advocating for wage parity among healthcare sectors and other opportunities for compensation for home care workers is necessary. Additional strategies include supportive and innovative approaches for scheduling, teamwork, and working with staff to identify barriers and solutions in home care.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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 ».