Quality of life of Venezuelan migrants in Brazil during the COVID-19 pandemic.
Bibliographic record
Abstract
The economic, social, and health crisis in Venezuela has resulted in the largest forced migration in recent Latin American history. The general scenario in host countries influence migrants' self-perception of quality of life, which can be understood as an indicator of their level of integration. The COVID-19 pandemic has exacerbated socioeconomic and health vulnerabilities, especially for forced migrants. We hypothesized that the adverse circumstances faced by Venezuelan migrants during the pandemic have deepened their vulnerability, which may have influenced their perception of quality of life. This study aims to evaluate the quality of life of Venezuelan migrants in Brazil during the COVID-19 pandemic. We assessed the quality of life of 312 adult Venezuelan migrants living in Brazil using the World Health Organization WHOQOL-BREF quality of life assessment, which was self-administered online from October 20, 2020, to May 10, 2021. The associations of quality of life and its domains with participants' characteristics were analyzed via multiple linear regression models. Mean quality of life score was 44.7 (±21.8) on a scale of 0 to 100. The best recorded mean was in the physical domain (66.2±17.8) and the worst in the environmental domain (51.1±14.6). The worst quality of life was associated with being a woman, not living with a partner, lower household income, and discrimination based on nationality. Factors associated with overall quality of life and respective domains, especially income and discrimination, were also observed in other studies as obstacles to Venezuelan migrants. The unsatisfactory quality of life among Venezuelans living in Brazil may have been worsened by the pandemic during the study period.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".