Occupational stressors and coping mechanisms among obstetrical nursing staff during the COVID-19 pandemic: a qualitative study
Notice bibliographique
Résumé
BACKGROUND: Due to heightened occupational stress throughout the COVID-19 pandemic, hospital nurses have experienced high rates of depression, anxiety, and burnout. Nurses in obstetrical departments faced unique challenges, such as the management of COVID-19 infection in pregnancy with limited evidence-based protocols and the unknown risks of the virus on pregnancy and fetal development. Despite evidence that obstetrical nurses have experienced high levels of job stress and a decrease in job satisfaction during the COVID-19 pandemic, there is less known about the working conditions resulting in these changes. Using the Job Demands-Resources (JD-R) model, this study aims to offer insight into the COVID-19 working environment of obstetrical nurses and shed light on their COVID-19 working experiences. METHODS: The study was conducted using a qualitative approach, with data collection occurring through semi-structured interviews from December 2021 to June 2022. A total of 20 obstetrical nurses recruited from the obstetrical departments of a tertiary hospital located in Ontario, Canada, participated in the study. Interviews were audio-recorded, transcribed verbatim, and coded using NVivo. Data was analyzed using a theoretical thematic approach based on the JD-R model. RESULTS: Four themes were identified: (1) Job stressors, (2) Consequences of working during COVID-19, (3) Personal resources, and (4) Constructive feedback surrounding job resources. The findings show that obstetrical nurses faced several unique job stressors during the COVID-19 pandemic but were often left feeling inadequately supported and undervalued by hospital upper management. However, participants offered several suggestions on how they believe support could have been improved and shared insight on resources they personally used to cope with job stress during the pandemic. A model was created to demonstrate the clear linkage between the four main themes. CONCLUSIONS: This qualitative study can help inform hospital management and public policy on how to better support and meet the needs of nurses working in obstetrical care during pandemics. Moreover, applying the JD-R model offers both a novel and comprehensive look at how the COVID-19 hospital work environment has influenced obstetrical nurses' well-being and performance.
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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 | 0,004 |
| 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 | 0,001 | 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 ».