The Psychological Impact of COVID-19 Pandemic on the population of Bahrain.
Bibliographic record
Abstract
BACKGROUND AND AIM: The pandemic of COVID-19 is a global crisis that is considered a stressful event directly and indirectly (via prophylactic measures taken) for people in any society. It can have an impact on mental health resulting in a plethora of symptoms. METHOD: This study measures the psychological impact, demonstrated by the symptoms of depression, anxiety, and stress. An online semi-structured questionnaire has been used with all participants, and with the measure The Arabic version of The Depression Anxiety and Stress Scale -21 (DASS-21). The study design was cross-sectional. Which was conducted in April-May 2020. The sample was (n=1115) from Bahrain's population, (1081 Bahraini) and (33 non-Bahraini), aged 18 and above, 701 females, most of them were graduated and employed. Results showed 30% were with depressive symptoms, 18.2% have exhibited symptoms, and 30.8% reported stress symptoms. Females were higher than males in depressive and anxiety symptoms. While no gender differences in stress symptoms. The younger age group showed more distress across the board with symptoms reported decreasing with age. Students were also noticed to be the group reporting the highest symptoms, together with people with the lowest income. ConclusionThe study has demonstrated a high psychological impact on the population of Bahrain with around a third of the population demonstrating some level of distress.
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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 | 0.000 | 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) | 0.002 | 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".