Psychological consequences and the related factors among COVID‐19 survivors in southeastern Iran
Bibliographic record
Abstract
Introduction: Coronavirus disease-2019 (COVID-19) is a new viral disease that has spread rapidly worldwide since December 2019 and there is no effective treatment for it. The current study aimed to investigate the psychological consequences and related factors among COVID-19 survivors. Methods: This descriptive analytical study was conducted on 152 patients with COVID-19 referred to referral hospitals in southeastern Iran in 2020. Data collection tools were three questionnaires of demographic and background information, Depression Anxiety stress Scale (DASS-21) and Impact of Events Scale-Revised (IESR). Descriptive and inferential statistics and SPSS25 were used to analyze the data. Results: The mean age of patients was 39.52 ± 13.16 years. The patients were mostly female (63.8%). Seventy-three percent of the patients had severe posttraumatic stress disorder, 26.3% had moderate depression and 26.3% had severe anxiety. The mean scores of posttraumatic stress, depression, and anxiety among patients with COVID-19 were 41.59 ± 17.28, 12.13 ± 9.16, and 12.45 ± 10.71, respectively. Intensive care unit (ICU) admission, divorce, illiteracy, and retirement were all associated with higher psychological load among patients. Discussion and Conclusion: The results showed that patients with COVID-19 had different levels of anxiety, depression, and posttraumatic stress. These results may direct the attention of the medical staff to the mental health of COVID-19 patients, necessitating timely psychological care and intervention during an epidemic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".