Tracking the Trajectory and Predictors of Peritraumatic Distress among Chinese Migrants in Canada across the Three Years of the COVID-19 Pandemic
Bibliographic record
Abstract
Negative effects of the COVID-19 pandemic on mental health have been widely reported. Chinese populations, especially those living overseas, are highly vulnerable to mental health problems considering the unique challenges they faced during the pandemic. Even though the pandemic lasted for three years, little is known about the mental health condition of this special population over this time course. The current study aimed to assess peritraumatic distress among Chinese migrants in Canada and identify its consistent risk predictors across the three years of the pandemic (2020, 2021, 2022). Three groups of Chinese adult migrants (i.e., aged 18 or above) living in Canada were randomly recruited through social media and the internet to complete an online survey in 2020, 2021, and 2022 respectively. Peritraumatic distress was assessed with the COVID-19 Peritraumatic Distress Index (CPDI). Univariate analysis of variance (ANOVA) models and a subsequent hierarchical multiple regression model were conducted to track peritraumatic distress differences across the three years and identify potential risk factors. The results showed that the CPDI score increased from 2020 to 2022 and peaked in 2021. Age, birthplace, health status, perceived discrimination, self-contraction and family contraction worry were identified as significant sociodemographic and COVID-19-related predictors for peritraumatic distress (absolute βs = 2.16–9.00; ps < 0.05). The results provide insight into the mental health condition of overseas Chinese migrants across the three years of the pandemic.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| 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".