The COVID-19 Experience for the Family and Children: \nA Study of Iranian Immigrant Families in Montreal, Canada
Bibliographic record
Abstract
The COVID-19 pandemic has had a significant impact on children and families around the world. Iranian immigrant families in Montreal, Canada, have faced unique challenges related to social isolation, economic pressures, and difficulties accessing public services during this time. Pre-existing stressors, such as language barriers, cultural differences, and the integration process in the new country, compounded these challenges. Eight Iranian immigrant parents (seven mothers and one father) were interviewed regarding their experiences coping with the COVID-19 pandemic, in particular, the effects on the children and the strategies families used to cope with the pandemic. Finally, parents were interviewed regarding their perceptions and beliefs regarding exposing children to nature, its opportunities and challenges and one coping strategy. Parents reported feeling overwhelmed by the demands of managing their children's education at home while also trying to work and manage their own stress. Parents also stated that children, in turn, experienced feeling lonely and disconnected from their peers and struggling with the abrupt changes in their daily routines. Despite these challenges, the study also found that this population was resilient and resourceful, relying on their own networks of support and seeking out community resources to cope with the pandemic. Overall, the study highlights the need for policymakers and service providers to understand better the unique needs of immigrant families, including access to resources in multiple languages, addressing financial challenges, and mental health support during the pandemic.
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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 | 0.002 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".