Mental distress and psychological disorders related to COVID-19 mandatory quarantine
Bibliographic record
Abstract
Abstract Background:Restrictions that were enforced from COVID-19 have affected many people’s lifestyles and ability to earn. They caused a distress that even added to the war stress that the Syrian population has encountered for the last 9 years. This study aims to assess this distress and the major concerns of the people related to COVID-19.Methods:Online questionnaires were distributed using SPTSS, K10, and MSPSS were used with other demographic, war- and COVID-19-related questions that were taken from The (CRISIS) V0.1 Adult Self-Report Baseline Form.Results:Our sample included 5588 with the mean age of 26.84 ± 7.815 years. Of those, only one was a confirmed COVID-19 case. Over 42.7% had two or more positive PTSD symptoms, 42.6% had moderate or severe mental disorder, but only 14.9% had low social support. Higher PTSD and K10 scores overall were seen in females and with most of war variables (P<0.05). Relationship with partner were negatively affected in most and distress from the declined ability to work and provide food were the most prominent.Conclusions:The indirect effect of COVID-19 is far more than that of the pathogen itself. Reduced ability to earn and to provide food was the main concerns. Relationships deteriorated mainly in participants with high K10 and PTSD scores who also felt more symptoms and used more hypnotics in the last 4 weeks. Smoking changes were not related to K10 and PTSD. Social support played a role in reducing stress, but with the relationships being affected, lower support was seen.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".