Emotional experiences and coping styles of Brazilians in Canada during the Covid-19 pandemic facing stressful situations with their family and friends in Canada and Brazil
Bibliographic record
Abstract
Objective: to identify the emotional experiences and coping strategies of Brazilians in Canada during the COVID-19 pandemic in the face of stressful situations for themselves, their families, and friends in Canada and Brazil. Methods: an online survey (July – October 2021) with an original questionnaire was used with Brazilians living in Canada to explore emotional experiences during the COVID-19 pandemic and their coping strategies in the face of stressful situations. Data analysis was descriptive statistics. Results: participants (n=387) reported that main worries related to the safety of significant ones living in Brazil due to high risk of infection, lack of self-protection, as well as restricted access to healthcare services. Results: identified individuals’ emotional experiences and the strategies they used to deal with them. Experiences seemed to be less stressful and relatively manageable through the use of social media, physical exercises, contact with nature, and reconnection with religion and spirituality. Feelings of worry and fear and efforts at problem-solving arose for Brazilians in Canada mainly from issues lived by their families and friends in Brazil. A sense of safety regarding themselves, their friends, and relatives in Canada was also identified. Conclusions: results corroborated that the COVID -19 pandemic brought uncomfortable emotions in response to stressful and uncertain situations. Respondents acknowledged their experience with these types of emotions and reported diverse adaptive strategies depending on their approach to the situation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".