Psychosocial Impact of the COVID-19 Pandemic on the Adult Population of Buenos Aires
Bibliographic record
Abstract
Background:In March 2020 the World Health Organization (WHO) declared the SARS-CoV-2 coronavirus a pandemic, and since then a remarkably large psychological experiment has been carried out in the world: social isolation.Objectives: The aim of this study was to analyze the impact of social isolation on healthy habits and some psychosocial and behavioral aspects during the confinement and restrictions imposed by the pandemic in the metropolitan area of Buenos Aires (AMBA).Methods: An anonymous survey, excluding medical personnel, was carried out through social networks (WhatsApp, Instagram, Facebook and e-mail), Results: After 7 days of sending the link 2,912 people had answered the survey.Age was between 40 and 60 years in 48.2% of participants, with a predominance of women.In 43.53% of cases, respondents perceived changes in their lifestyle, such as a twofold increase of hours in front of electronic devices during quarantine.This was accompanied by a more sedentary lifestyle, since 83.5% exercised before the pandemic but only 6.4% maintained the prior hours of weekly physical activity.Altered eating habits was reported by 43.52% of participants and 41% referred symptoms compatible with depression, anxiety, sadness, reluctance or hopelessness.Conclusions: Our study suggests that psychological wellbeing and healthy habits are threatened by confinement in the face of the COVID-19 pandemic, so it is necessary to implement measures to prevent consequences in our population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".