Depression, Anxiety, and Stress among the Community during COVID-19 Lockdown in Saudi Arabia
Bibliographic record
Abstract
Objectives: (1) We aimed to measure the levels of depression, anxiety, and stress among the Saudi population during COVID-19 lockdown and their association with different personal characteristics. (2) The secondary aims included assessing the perceived social and physical impacts of COVID-19 lockdown on individuals and the different coping strategy practices during this tough period. Methods: A cross-sectional study was conducted between May and June 2020 in Saudi Arabia. We collected data from both sexes aged 18 years and older using social media. The online questionnaire collected data on their sociodemographic, physical, and social conditions, and the presence and control of chronic diseases as well as their evaluation according to the Depression, Anxiety, and Stress Scale-21. Results: Of the 878 participants, 56.6% were female, 54.6% were aged between 35 and below, 52.6% were married, and 97.4% had a secondary school and above. Approximately a quarter of the participants or relatives had been diagnosed with COVID-19. Moderate-to-severe depression, anxiety, and stress were reported in 32.6%, 28.7%, and 22.6% of the participants, respectively. The younger than 35 years, unmarried, not working, and the previous diagnosis of COVID-19 were associated with higher scores of depression, anxiety, and stress. In addition, the participants reported several coping strategies such as doing physical exercise, hobbies, chatting over social media, watching TV/movies, playing electronic games, increasing religious prayers, and getting psychosocial help. Conclusion: A quarter of the participants reported a moderate-to-severe psychological impact. They adopted various strategies to reduce the adverse lockdown effect. In a future pandemic, health-care providers and policymakers can focus on potential risk factors and coping strategies to prevent, intervene early, and treat sufferers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".