Correlation of Depression, Anxiety and Stress with Quality of Life in COVID-19 Pandemic
Bibliographic record
Abstract
Due to Covid-19 pandemic the psychological health of individuals is disturbed globally. There is a dire need of looking into details about the effects of mental health issues on quality of life (QOL). Objectives: To determine correlation between depression, anxiety, stress, and quality of life among adults in Covid-19 and evaluate the impact of demographics on quality of life. Methods: It was a cross-sectional study carried out at a tertiary care hospital. Patients presenting in the psychiatry outdoor of age 18 to 60 years, of both genders and scoring ≥21 on Depression, Anxiety, and Stress Scale (DASS) were enrolled in the study and depression, anxiety and stress severity was assessed and Quality of Life Scale (QOLS) was applied on all to assess their quality of life. All findings were then subjected to statistical analysis. Results: The mean age of the patients was 21.78±3.204, mean depression score on DASS was 8.58±4.510, mean anxiety score on DASS was 11.68±4.160 and the mean stress score on DASS was 14.84±3.192. There were 63.5% males and 36.5% females. Depression, anxiety and stress had a negative correlation with quality of life. Depression and stress were significantly correlated negatively with quality of life (p=0.000). No demographical factor was significantly associated with poor quality of life. Conclusion: Depression, anxiety and stress were negatively correlated with poorer QOL and depression and stress had significant association with poor QOL. Key words: Anxiety, Depression, Covid-19, Quality of life How to cite: Rashid A., Mudassar U., Tariq I., Zaheer A., Iftikhar M., Mazhar N. Correlation of Depression, Anxiety and Stress with Quality of Life in COVID-19 Pandemic. Esculapio 2021;17(02):195-199.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".