Impact of Socio-Psychological Issues Among University Students During Lockdown in Karachi, Pakistan
Bibliographic record
Abstract
The rapid COVID-19 spread is instigating a tremendous amount of concern and anxiety among the people of Pakistan. Students from Pakistan's four most prestigious universities were surveyed for the cross-sectional web-based study. An online questionnaire was distributed (Generalized Anxiety Disorder (GAD) 7-item scale, depression (9-item PHQ (patient health questionnaire)), as well as discomfort sources (14-items) via Google forms. Total 100 replies (with an age of 21.7 years’ average and a range of 3.5 years, and a 70.5% female representation) were received. According to our findings, moderate-severe anxiety and sadness (a score of 10) were found in 34% and 46% of students, respectively. Population over the age of 31 had a much lower level of depression than those under the age of 30. In comparison to females, males were found to have considerably lesser levels of depression and anxiety. Established anxiety was found to be more prevalent among those who had a close family member, acquaintance, or friend who had a mental illness. COVID-19 pandemic's negative consequences on daily living followed by the rapid spread of the disease were the primary sources of distress. Students' mental health is negatively impacted by COVID-19. They use active coping, spiritual/ religious coping, self-distraction, and acceptance as the most typical coping methods. During the pandemic, it is suggested that pupils' mental health is not ignored. Keywords: COVID-19, Anxiety, Depression, University Students, Karachi, Pakistan DOI: 10.7176/JESD/13-6-10 Publication date: March 31 st 2022
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".