Sociodemographic and COVID-Related Predictors for Mental Health Condition of Mainland Chinese in Canada Amidst the Pandemic
Bibliographic record
Abstract
The current study investigates the mental health condition of Mainland Chinese in Canada and identifies the associated sociodemographic and COVID-19-related predictors. A sample of 471 Mainland Chinese aged 18 or older completed an online survey that collected information on demographics, experience, cognition, and behaviours related to the COVID-19 pandemic and mental health condition. Mental health condition was assessed with the Depression, Anxiety, and Stress Scale-21 (DASS-21) for the depression, anxiety, and stress levels of Mainland Chinese during the pandemic. Moderate to severe depression, anxiety, and stress levels were respectively reported by 11.30%, 10.83%, and 5.10% of respondents. Univariate analysis of variance models (ANOVAs) were conducted to assess mental health condition variance as stratified by independent sociodemographic- or COVID-19-related explanatory variables, to identify possible predictors to be entered into the subsequent regression models. The regression models identified age, income level, health status, and perceived discrimination as significant sociodemographic predictors (absolute value of βs = 1.19–7.11, ps < 0.05), whereas self-infection worry, attitude towards Canadian measures, information confusion, food/goods stocking, and room cleaning/sanitizing were identified as significant COVID-19-reltaed predictors (absolute value of βs = 1.33–3.45, ps < 0.05) for mental health outcomes. The results shed light on our understanding of the major factors associated with the mental health condition of Mainland Chinese in Canada during the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".