Critical Care Nurses as Frontline Warriors During COVID-19 in Pakistan
Bibliographic record
Abstract
Background: The COVID-19 pandemic resulted in several short-term and long-term impacts on the physical, social, and psychological health of every individual globally, especially among frontline workers including nurses working in the critical care settings. In Pakistan, 85,264 confirmed cases have been identified and 1,770 deaths are reported so far. The death rate is 2.0% as compared to Europe (14.6%) and the United States (5.7%) per 100,000 population. Frontline workers are the most vulnerable population during this pandemic. Nearly 440 frontline staff including nurses, doctors, and other health workers have been infected with COVID-19 with 8 confirmed deaths reported in different provinces of Pakistan. These numbers are continuously increasing posing a serious threat for the health and well-being of the healthcare professionals especially nurses working in the critical care settings. Objective: The paper outlines the challenges and experiences of critical care nurses working in acute hospital settings of a low resourced country Pakistan during a pandemic. Methods: Literature search using CINAHL, MEDLINE, PubMed databases, local and international news papers, magazines, websites, international nursing colleagues and personal experiences/insights are included in the paper. Results: Findings include common challenges such as lack of staff, lack of personal protective equipment (PPE), limited knowledge regarding standard infection control practices, isolation protocols, lack of administrative support, transportation, accommodation, childcare facility, and so forth. As a result, most nurses are reporting symptoms of fear, anxiety, depression, post-traumatic symptoms, spiritual, and moral distress. Nurses often become targets of violence and harassment by the general public in the Pakistani healthcare system, due to lack of awareness, cultural beliefs, low status/image of nurses, low literacy levels, and poverty. Conclusion: Targeted interventions and policies are needed to maintain safety and protect physical, social, psychological, and spiritual health and well-being of nurses. Health authorities in Pakistan should take the responsibility in creating awareness, providing adequate guidance, and support to enhance nurses' well-being and quality of life during the pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".