Impacts of COVID-19 on Brazilians living abroad and the social determinants of their emotional health
Bibliographic record
Abstract
Objective: to describe the experiences of Brazilians living in Canada during the COVID-19 pandemic and highlight its impact on their social determinants of emotional health. Method: an online survey as a part of a convergent mixed-methods design. Participants were Brazilians living in Canadian urban areas throughout the COVID-19 pandemic (May-October 2021). An original questionnaire was created in Portuguese, English, and French. A sample of 113 respondents answered the questionnaire’s closing question: “For us to better understand the impact of COVID-19 on your life, you are welcome to share any additional information about your experiences.” The thematic analysis was applied. Results: narrative responses reveal no sociodemographic identification. Reported impacts were about the determinants of health regarding participants’ coping mechanisms and the impacts on emotional life, emotional stability, social support and the impact on feelings of belonging, as well as multiple gains in life. The evidence indicates that emotional health was preserved for most respondents. Conclusion: the stress related to the pandemic had a significant impact on the mental and physical health of Brazilians in Canada. Uncertainties resulting from the pandemic and government response measures resulted in concerns about one’s own health and that of family members, friends, co-workers, and the community in general.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".