Examining changes in self-reported mental health among selected Asian and White populations in Canada between 2019 to 2022: A retrospective study
Bibliographic record
Abstract
Introduction: Research has shown that the mental health of Canadians has been negatively impacted during the pandemic, particularly within racialized populations. This impact may be especially true among Chinese (due to xenophobia) and South Asian people (due to high COVID-19 infection) in Canada. This study examines the change in self-reported mental health among Chinese, South Asian, and Other Southeast and East Asian populations compared to the White population by immigration status before and during the pandemic. Methods: This retrospective, cross-sectional study used four cycles (2019 to 2022) of the Canadian Community Health Survey. Differences in high levels of mental health were compared using logistic regression, taking into consideration immigrant status and duration since landing, and controlled for demographic, socio-economic factors, and sense of belonging. Results: The overall Asian and White populations reported similar prevalence of high mental health. This was followed by a widening of the Asian-White differential, advantaging the Asian population. The Chinese sub-group had the lowest prevalence of high mental health pre-pandemic compared to their White counterparts, but the differential disappeared by 2022. Conclusion: Study results support an overall deterioration in mental health during the pandemic, especially among White and non-immigrant Asian populations, while highlighting a healthy immigrant effect among recent Asian immigrants. The low mental health level of the Other Southeast and East Asian non-immigrant group in 2022 warrants further exploration.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".