Impact of COVID-19 lockdown on quality of life in a literate population in Pakistan
Bibliographic record
Abstract
Background: Early in the COVID-19 pandemic in Pakistan, complete lockdown was imposed from 24 March 2020: offices, shopping malls, market places, etc. were affected. On 25 March, further restrictions were imposed: hospital outpatient departments were closed and there was a ban on public and private gatherings. The lockdown significantly slowed down economic activities, and halted recreational, educational and religious activities and social gatherings. Aims: To assess the impact of the COVID-19 lockdown on the quality of life of literate individuals in Pakistan. Methods: A cross-sectional, descriptive study was conducted from 25 April to 15 May, 2020 among literate Pakistani who understand the English language, aged 10+ years and had internet access. We selected 500 individuals to complete the McGill questionnaire online. Results: The response rate was 73% (n = 365): 49% males and 51% females. Around one third reported a moderate effect on overall quality of life. Financial life was moderately affected in 45% and both physical life and emotional life in 43% of participants. Spiritual life was excellent in 69%. However, social life was severely affected in 56%. Mild depression was felt by 47% of respondents and 48% felt strongly supported during the COVID-19 lockdown. Conclusion: The COVID-19 lockdown made little difference to the quality of life of the literate population of Pakistan. A few aspects were moderately affected and social life was badly affected. Spiritual life improved for most individuals.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".