Behavioral Impact of Lockdown Due to COVID-19 in Pakistan (Preprint)
Bibliographic record
Abstract
BACKGROUND Since it was first identified in December 2019 in Wuhan city of China, Covid-19 has spread across the globe. In absence of vaccination and effective treatment the mainstay of prevention is social distancing and other preventive measures to curtail spread of the disease. Most countries in the world have imposed some form of restrictions on travelling and businesses. Pakistani Government imposed a lockdown on March 23rd 2020. OBJECTIVE In this online survey we have collected data about individuals’ perception of the risks of the epidemic, adherence to the preventive practices, and emotional impact of the epidemic and lockdown. METHODS We collected data for this study through an online survey using an questionnaire. RESULTS Nine hundred and fifty-two individuals responded to the survey. There was a strong support for the lockdown. There was a realistic understanding of the risk and participants reported adherence to the preventive measures. Participants were going out of their houses for the purpose of work and buying groceries mostly. There was an emotional impact in the form of self-reported anxiety, depression and anger feelings. CONCLUSIONS Participants report adherence to preventive measures apart from going out of their houses out of necessities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".