Impact of COVID-19 Pandemic on the Quality of Life of Nurses Working in the Public Sector Tertiary Care Hospitals of Karachi
Bibliographic record
Abstract
COVID-19 has a substantial influence related to the quality of life of nurses by increasing the number of patients, which increases the work burden and stress level. Objective: To determine the impact of the COVID-19 pandemic on the quality of life of nurses working in the public sector tertiary care hospitals of Karachi. Methods: Present an analytical cross-sectional study design was employed to determine the quality of life of working by using a non-probability convenient sampling technique to recruit 240 nurses. The quality of life of nurses was assessed by using the McGill Quality of Life (QoL) revised questionnaire. Results were considered significant at p-value of ≤0.05. Results: Out of a total of 240 nurses, most of them 135 (56.2%) were male, 177(73.88%) married, 128, 53.3% Post RN qualification, and 99 (41.2%) 6 to 10 years of working experience. The mean+SD of the overall QoL of nurses was 6.56+2.53. Based on multiple logistic regression analysis, males had 2.79 times better QOL during the COVID-19 pandemic as compared to females (ORadj=2.79, 95% CI: 1.05 - 7.45, p= 0.04). Similarly, married persons had 3.06 times better QOL during the COVID-19 pandemic as compared to others (ORadj=3.06, 95% CI: 2.14 – 3.34, p= 0.003). Conclusions: It is concluded that the COVID-19 pandemic has a significant effect on all aspects of the physical, psychological, existential and social quality of life of nurses working in the public sector tertiary care hospitals of Karachi, Pakistan
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".