The IMPACT Cross-Sectional Study: The socioeconomic experiences of US and non-US immigrants in Canada in the midst of the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background: The COVID-19 pandemic has exacerbated socioeconomic deficiencies within Canada's immigrant populations, yet the nature of these challenges is not well understood. Specifically, it is essential to understand the difference between immigrants from countries with similar language and resources (such as the US) and those from a different background (non-US countries). Accordingly, the IMPACT study at the centre of this article included a Canadian national survey that compared key domains of life in US immigrants with non-US immigrants to provide policymakers with a research-based path toward delivering culturally targeted and socially competent services. Methods: Potential participants were recruited from various newcomer support services centers in Canada to complete the IMPACT survey. The survey comprised a series of questionnaires designed to assess participants' self-perceived impacts of COVID-19 on various socioeconomic markers. For each socioeconomic variable, we analyzed the experiential differences between US vs non-US immigrant subgroups. A chi-square analysis was used to analyze the differences between these geographic subgroups (significance level α=0.05). Results: On average, non-US immigrants in Canada were less likely to disclose their COVID-19 health status than their US-based counterparts; this trend was correlated with reported concerns over discontinuation of one’s income. Qualitative themes within the non-US immigrant subgroup elucidated a mentality of “making it on [one’s]own”, and consequently, a reluctance to seek out external resources. Surprisingly, despite the US immigrant subgroup having better socioeconomic conditions before the onset of the pandemic, this subgroup was subject to a comparatively greater post-pandemic decrease in socioeconomic well-being, resulting in proportionally greater food and financial insecurities than non-US immigrants. Conclusion: The study highlighted two key findings: (1) US immigrants faced a proportionally increased instability of their socioeconomic well-being; whilst (2) non-US immigrants faced greater social and intrapersonal barriers to external supports and experienced a greater incidence of COVID-19 infections, likely resulting from this cohorts reluctance to miss work on the basis of income generation. Government-funded immigration resources, newcomer support centers, and researchers require evidence-based, demographically-targeted initiatives to aid the diverse needs of Canadian immigrants and in the post-pandemic recovery period and in future public health emergencies.
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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 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".