Comparison of the Quality of Life, Mental Health, Alexithymia and Distress Tolerance of Nurses Working in Intensive Care Units of Covid-19 (Corona) Patients with Nurses in Other Wards
Bibliographic record
Abstract
The aim of this study was to compare the quality of life, mental health, and alexithymia and Distress tolerance of nurses working in intensive care units of Covid-19 (Corona) patients with nurses in other wards. Which was done by descriptive method of causal-comparative studies. The statistical population of this study was selected from all hospitals in Tehran's third district and the sample size was equal to 97 people (47 nurses in the intensive care unit and 50 nurses in the normal ward). Data collection tools in this study included the World Health Organization Quality of Life Questionnaire (short form) (1989), Goldberg and Hiller General Health Questionnaire (1979), Toronto Bagby et al (1994). Emotional Dysfunction Scale, Simmons and Gaher(2005). Methods of data analysis in this study using the questionnaires in the descriptive section including describing the demographic characteristics of the sample group, mean, standard deviation, and preparing a frequency distribution table, statistical graphs, and in the analysis of hypotheses. Achieving the result was analyzed using independent t-test by SPSS22 software. The results showed that there was a significant difference between quality of life in nurses working in intensive care units and nurses working in normal wards. The quality of life of nurses working in normal wards is higher than nurses in special wards and also mental health, emotional malaise and tolerance of anxiety of nurses working in special wards are higher than nurses in normal wards. According to the results of the present study, providing adequate personal protective equipment and creating the conditions for using counseling and psychological services in a timely manner, along with other security and incentive measures, can be useful in reducing the psychological overload of nurses.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".