Social Consequences of the COVID-19 Pandemic. A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: To provide a systematic review of the social consequences of COVID-19 pandemic. METHODS: In the present study, articles indexed in Persian and Latin databases (Web Of Science, Scopus, PubMed, Embase, Google Scholar and Magiran). 43 documents published in the last 3 years in Persian or English language were reviewed. The research steps were performed according to PRISMA writing standard and the quality assessment was done by two researchers independently with Newcastle Ottawa Scale tools for observational studies according to the inclusion criteria. RESULTS: Measures to break the chain of virus transmission and to control the COVID-19 pandemic have caused major problems in the economic, social, political and psychological spheres and have affected billions of people worldwide. The COVID-19 pandemic crisis has caused widespread unrest in society and unprecedented changes in lifestyle, work and social interactions, and increasing social distance has severely affected human relations. CONCLUSIONS: The COVID-19 pandemic has social consequences in certain groups can exacerbate their unfavorable situation. Special groups in crisis situations should be given more attention, and clear and precise policies and programs should be developed to support them.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".