Promoting resilience and mental well-being among immigrants in Regina, Saskatchewan, Canada, during the COVID-19 pandemic: a qualitative study
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic had significant impacts on the lives and mental health of individuals across the globe. Due to language barriers and social, economic, and cultural factors, these challenges were amplified for immigrants to Canada during the pandemic, putting them in an increasingly vulnerable position. THE OBJECTIVE: Of the study was to document the challenges experienced, the impacts on mental health, and other aspects of life, and support immigrants during the COVID-19 pandemic by engaging new immigrants, using a virtual platform that offered a new approach for collaboration. METHODOLOGY: Taking a community-based research approach in collaboration with the Regina Immigrant Women Center, 14 language-assisted discussion sessions were hosted virtually between July 2020 and April 2021. The sessions covered credible and current public health measures, and participants collectively discussed strategies to address upcoming challenges posed by the pandemic. Discussion on daily life challenges imposed by the pandemic and solutions implemented served as data. RESULTS: Thematic analysis of participants' perspectives highlighted the impact of social isolation on all age groups. Overall, participants mentioned considerable mental stress amplified by uncertainty, fear of infections, and social isolation. Negative influences of social media and technology use on mental well-being were highlighted. Participants suggested various coping strategies, including religious and spiritual practices, connecting virtually, expressing gratitude, positive self-talk, self-love, and self-care for mental well-being. Participants also shared lessons learned and insights discovered during the pandemic. CONCLUSION: The interactive virtual discussion sessions helped maintain social connectedness, provided support and a sense of community for immigrants, enhancing resilience, and positively impacting mental wellness. Language-assisted virtual discussion sessions can support immigrants during a health emergency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".