Prevalence of depression and anxiety among general population in Pakistan during COVID-19 lockdown: An online-survey
Bibliographic record
Abstract
Abstract The present study's aim is to find the prevalence of two of the common indicators of mental health - depression and anxiety – and any correlation with socio-demographic indicators in the Pakistani population during the lockdown from 5 May to 25 July 2020. A cross-sectional survey was conducted using an online questionnaire sent to volunteer participants. A total of 1047 participants over 18 were recruited through convenience sampling. The survey targeted depression and anxiety levels, which were measured using a 14 item self-reporting Hospital Anxiety and Depression Scale (HADS). Out of the total sample population (N=354), 39.9% suffered from depression and 57.7% from anxiety. Binary logistical regressions indicated significant predictive associations of gender (OR=1.410), education (OR=9.311), residence (OR=0.370), household income (OR=0.579), previous psychiatric problems (OR=1.671), and previous psychiatric medication (OR=2.641). These were the key factors e associated with a significant increase in depression. Increases in anxiety levels were significantly linked to gender (OR=2.427), residence (OR=0.619), previous psychiatric problems (OR=1.166), and previous psychiatric medication (OR=7.330). These results suggest depression and anxiety were prevalent among the Pakistani population during the lockdown. Along with other measures to contain the spread of COVID-19, citizens' mental health needs the Pakistani government's urgent attention as well as that of mental health experts. Further large-scale, such as healthcare practitioners, should be undertaken to identify other mental health indicators that need to be monitored.
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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 | 0.000 |
| 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 | 0.000 |
| 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".