Lockdown Policy Dilemma: COVID-19 Pandemic versus Economy and Mental Health
Bibliographic record
Abstract
Lockdown is considered to be the best of policies around the world to fight the deadly virus of COVID-19 which decimated hundreds of people in the last six months. However, this is not a cost-free measure. Billions of dollars worth of economic activities halted hinging upon these measures imposed by the governments of the countries. For instance, IMF predicted that the GDP growth will decline by 4.9 percent in 2020. Global trade is also expected to plummet by 27 percent in the second quarter of the year. In addition, paucity of recreational activities severely affects the mental health of the people. While imposing lockdown, both the cost and benefit should be analyzed to understand the real benefit of these measures on human life. This study critically examines the impact of the lockdown measures on mental health, and the economy of Bangladesh along with the efficacy of the measures on containing the virus. We found that the negative impact on the economy and mental health surpasses the positive impact of curbing the pandemic. It also compares the efficacy of the measures in different countries to find out the pattern that resembles with Bangladesh. From all the data, we conclude that the cost of lockdown measures in the country is greater than the benefit it brings to Bangladesh.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".