Experience of non-ICU nurses and factors influencing the resiliency while working in ICU during the covid-19 pandemic in a tertiary hospital
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".