Discrimination, Personal Discrimination, and Group Discrimination among Chinese \nCanadians/immigrants during the COVID-19 pandemic \n– results from an online cross-section survey
Bibliographic record
Abstract
Background: Pre-existing racial discrimination has been exacerbated, particularly among \nChinese immigrants in Canada since 2020, during the outbreak of the COVID-19 pandemic. \nMethod: Data for this cross-section 2021 study were collected via an anonymous online survey \nin both English and Chinese, with 739 participants aged 16 or older of Chinese origin residing \nin Canada for at least six months. Voluntary participation was ensured, with informed consent \nobtained prior to questionnaire access. The study utilized descriptive statistics for \nsociodemographic and mental health variables, Chi-square analysis for pre- and duringpandemic \ncomparisons, correlation analyses for examining relationships among variables, and \nconfirmatory factor analysis (CFA) on outcome variables. Mediating effects of perceived group \ndiscrimination were tested using model analysis and Bootstrap estimation procedure in AMOS. \nResults: There was a significant increase in reported discrimination experiences, with over \nhalf of participants experiencing discrimination, a 16.67-fold increase since the pandemic's \nonset. Public places were the most common sites for discrimination incidents. Only 7% of \nvictims reported incidents to authorities, citing barriers such as lack of knowledge (30.96%), \nsafety concerns (28.60%), and language barriers (27.41%). Both personal and group \ndiscrimination predicted poorer mental health outcomes, with over 80% reporting strong \nnegative emotions, primarily anger (95.33%). Perceived group discrimination partially \nmediated the relationship between personal discrimination and negative emotions. Covariates \nrevealed that higher education and English proficiency were associated with lower perceived \ngroup discrimination, while employment was linked to higher perceived group discrimination. \nPerceived discrimination positively correlated with perceived group discrimination, and both \nwere associated with negative emotions. \nConclusion: The study's findings underscore a concerning trend of escalating and widespread \nanti-Asian discrimination in Canada. Chinese immigrants lack awareness of available antidiscrimination \nresources, hindering effective response to incidents. Over 80% of respondents \nexpress skepticism about imminent change. Those experiencing discrimination exhibit \ndeteriorating mental health and diminished optimism. Many attribute the surge in \ndiscrimination to COVID-19 and suggest ad hoc laws as a solution; however, establishing a \nreliable reporting system emerges as a top priority from our discussion.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".