Factors Associated With Work Engagement of Nurses During the Fifth Wave of the COVID-19 Pandemic in Japan: Web-Based Cross-Sectional Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic has brought to light the prevalence of mental health issues among nurses. Work engagement (WE) is a concept that describes work-related positive psychological states and is of importance within mental health measures. There is, however, a lack of research on factors associated with the WE of nurses during the COVID-19 pandemic. OBJECTIVE: We aimed to determine which factors are associated with WE among nurses during the COVID-19 pandemic using the job demands-resources (JD-R) model as a framework. METHODS: A web-based cross-sectional survey was conducted among nurses working in acute care and psychiatric institutions in the prefectures of Chiba and Tokyo in Japan. The survey period occurred between August 8 and September 30, 2021, during a time when the number of patients with a positive COVID-19 infection increased. The 3-item version of the Utrecht Work Engagement Scale (UWES-3) was used to measure WE. Factors such as age, gender, years of experience, affiliated ward, COVID-19-related stress, financial rewards from the government and hospital, encouragement from the government and patients, and workplace social capital were assessed. A total of 187 participants were included in the final analysis. Multiple regression analysis was performed to examine the factors related to WE. Partial regression coefficients (B), 95% CI, and P values were calculated. RESULTS: The mean overall score for the UWES-3 was 3.19 (SD 1.21). Factors negatively associated with UWES-3 were COVID-19-related stress on work motivation and escape behavior (Β -0.16, 95% CI -0.24 to -0.090; P<.001), and factors positively associated with UWES-3 were affiliation of intensive care units (Β 0.76, 95% CI 0.020-1.50; P=.045) and financial rewards from the government and hospital (Β 0.40, 95% CI 0.040-0.76; P=.03). CONCLUSIONS: This study examined factors related to WE among nurses during the COVID-19 pandemic using the JD-R model. When compared with findings from previous studies, our results suggest that nurses' WE was lower than before the COVID-19 pandemic. Negative motivation and escape behaviors related to COVID-19 were negatively associated with WE, while there were positive associations with financial rewards from the government and hospital and affiliation with an intensive care unit. Further research into larger populations is needed to confirm these findings.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».