Characteristics Associated with Fear of COVID-19 among Syrian Refugee Parents in Canada
Bibliographic record
Abstract
Introduction: The aim was to assess the prevalence and factors associated with fear of COVID-19 among Syrian refugee parents in Ontario, Canada. Methods: A sample of 540 Syrian refugee parents who resettled in Ontario were interviewed between March 2021, and March 2022. The level of fear was measured using the Fear of COVID-19 scale. Multiple linear regression analysis was performed to assess the relationships between socio-demographic, migration, and health-related factors and fear of COVID-19. Results: The mean (SD) score for the Fear of COVID-19 scale was 15.6 (6.02), and 15.4% of the participants were categorized as having high levels of Fear of COVID-19. Results of the multiple linear regression analysis showed that low self-rated English/French language ability was significantly associated with increased fear of COVID-19 (Adjβ=0.65, p=0.047). When compared to participants who do not need an interpreter, those who needed an interpreter, and were always provided with one, were at reduced fear of COVID-19 (Adjβ=-1.56, p=0.061). In addition, findings indicated that low self-perceived socioeconomic status, more years spent in Canada, living in a refugee camp, and poor self-rated mental health contributed significantly to elevated levels of fear of COVID-19. Discussion: Targeted intervention and prevention strategies for reducing the fear of COVID-19 should be considered for the Syrian refugee population in Canada. Language ability is one of the factors related to increased fear of COVID-19, thus, providing information and interventions in different languages is essential for this population.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".