Effect of mode of healthcare delivery on job satisfaction and intention to quit among nurses in Canada during the COVID-19 pandemic
Notice bibliographique
Résumé
The COVID-19 pandemic resulted in a major shift in the delivery of healthcare services with the adoption of care modalities to address the diverse needs of patients. Besides, nurses, the largest profession in the healthcare sector, were imposed with challenges caused by the pandemic that influenced their intention to leave their profession. The aim of the study was to examine the influence of mode of healthcare delivery on nurses' intention to quit job due to lack of satisfaction during the pandemic in Canada. This cross-sectional study utilized data from the Health Care Workers' Experiences During the Pandemic (SHCWEP) survey, conducted by Statistics Canada, that targeted healthcare workers aged 18 and over who resided in the ten provinces of Canada during the COVID-19 pandemic. The main outcome of the study was nurses' intention to quit within two years due to lack of job satisfaction. The mode of healthcare delivery was categorized into; in-person, online, or blended. Multivariable logistic regression was performed to examine the association between mode of healthcare delivery and intention to quit job after adjusting for sociodemographic, job-, and health-related factors. Analysis for the present study was restricted to 3,430 nurses, weighted to represent 353,980 Canadian nurses. Intention to quit job, within the next two years, due to lack of satisfaction was reported by 16.4% of the nurses. Results showed that when compared to participants who provided in-person healthcare services, those who delivered online or blended healthcare services were at decreased odds of intention to quit their job due to lack of job satisfaction (OR = 0.47, 95% CI: 0.43-0.50 and OR = 0.64, 95% CI: 0.61-0.67, respectively). Findings from this study can inform interventions and policy reforms to address nurses' needs and provide organizational support to enhance their retention and improve patient care during times of crisis.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».