Factors Associated with Psychological Distress during COVID-19: A Cross-Sectional Study of Sub-Saharan African Migrant Workers across Australia and Canada
Bibliographic record
Abstract
Objective: Ensuring the sustainability of the migrant workforce requires a comprehensive understanding of the psychological challenges faced by this sub-population due to concerns about the wellbeing and financial situation of family members in their home countries. Therefore, this study investigates the factors associated with psychological distress among sub-Saharan Africa (SSA) migrant workers across Australia and Canada during the COVID-19 pandemic. Method: Data were collected from 378 first-generation migrant workers with SSA ancestry residing in Australia and Canada using the Depression Anxiety and Stress Scale 21 (DASS-21). Multivariate logistic regression analysis was used to determine socio-demographic factors associated with depression, anxiety, and stress among SSA migrants’ populations. Results: Across both countries, migrants with lower levels of education were more prone to reporting feelings of depression, anxiety, and stress during the pandemic. Female participants in Australia were more likely to report feeling of depression. Participants in Australia and Canada who were separated/divorced/widowed were less likely to report stress and depression, respectively. Participants in Australia who had lived in Australia between 11 and 20 years and those between 36 and 50 years old were more likely to report feelings of depression. Participants residing in Australia whose SSA ancestry was Southern Africa/Central Africa were more likely to report anxiety. Participants in Australia who worked as part-time permanent workers and those who worked as fixed-term workers/short-term/casual workers were less likely to report anxiety. Finally, participants in Canada who reported two or more people living with them had higher odds of reporting anxiety. Conclusions: The findings from this study highlight key factors associated with SSA migrant workers’ psychological distress during the pandemic. The results can inform policies and provide insight to the development of mental health intervention strategies for migrant workers to minimize similar distress during pandemics.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".