Experience of non-ICU nurses and factors influencing the resiliency while working in ICU during the covid-19 pandemic in a tertiary hospital
Notice bibliographique
Résumé
Background and objective: The coronavirus outbreak in 2019 has created unprecedented pressure on health care staff and material resources such as PPEs, Ventilators, Oxygen supplies, hospital beds etc. Ensuring an adequate supply of nurses to maintain a high standard of care and safe infective care practices in the phase of the increased patient load is a huge challenge for all stakeholders. Utilizing non-ICU nurses for ICU care is an option. However, factors that influence the optimal selection and coping behaviour (resilience) of a non-ICU nurse are not well examined. In this paper, we “adopt a mixed method design” to determine the suitable specialty staff for ICU attachment during a pandemic. I will emphasise the significance of educational training and preparation of critical care on non-ICU nursing staff in relation to their adaption and coping level throughout this study. The objectives of this study were (1) to explore experiences, perceptions and factors influencing resilience of non-ICU nurses during the COVID-19 pandemic and (2) to review the lived experience of non-ICU nurses after the critical care competency training programme.Methods: After obtaining the comments from the Dissertation Review Board, the study adopted a mixed method study design. The authors selected 76 samples (eight males and sixty-eight females) by “non-probability convenient sampling”. The authors used a survey for data collection lasting 8 weeks. The authors used descriptive (frequency, percentage distribution, mean and standard deviation) and inferential statistics to analyse the data collected.Results: The study revealed that most of the staff (75%) met the prepared objectives of the orientation program. Approximately 90% of the staff agreed that they are able to take care of critically ill patients with minimal supervision. Further, 29% of the staff stated that psychological preparation & staff readiness are the first priorities to be considered before ICU attachment. We also evaluated the ability of the staff to bear the ICU workload, and 51% of the staff reported it being bearable. This number is similar to the number of staff who reported suitable health status to physical exertion needs of the ICU. Staff age, marital status, gender, qualification, area of experience, and years of experience did not influence staff coping mechanisms. However, the attachment staff with previous ICU exposure have effective coping mechanisms during their attachment in ICU.Conclusions: In shortage of ICU staff in case of a pandemic, staff with neurology and neurosurgery background showed a higher confidence and coping level to ICU stressful environment. Furthermore, staff with other clinical backgrounds can work effectively during this circumstance with organized training, preparedness plan, effective clinical follow up and psychological support. All these factors facilitate the coping mechanism.
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 | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».