A database on the socioeconomic and behavioral impact in Sri Lanka through multiple waves of COVID-19
Bibliographic record
Abstract
Abstract Objectives: The impact of the COVID-19 pandemic was diverse and disproportionate among nations and population segments. The impacts of the disease and the containment strategies adopted are broad and cut across multiple facets of life, society, and the economy, which are intimately interlinked. To ascertain the socioeconomic impact and human behavior changes due to the pandemic and the containment strategies adopted a large household survey was conducted covering all the provinces in Sri Lanka. Data description: We conducted a cross-sectional Household survey covering all 9 provinces, including 20 districts in Sri Lanka from August 2021 to September 2021. This dataset consists of the data collected from 3020 households, on the impact of the pandemic through three distinctly identified pandemic waves in Sri Lanka. The questionnaire was designed to capture COVID-19 impact in 2 primary sections (socioeconomic impact and behavioral impact) which were further divided into 8 sub-sections: educational impact, impact on mobility, access to health services, economic impact, human interactions, food consumption, religious and cultural, and psychological impact. This dataset will enable researchers and policymakers to analyze the impact of the pandemic through a multifaceted perspective enabling a more holistic approach to decision-making.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".