Peritraumatic Distress among Chinese Canadians during the Early Lockdown Stage of the COVID-19 Pandemic: Sociodemographic and Pandemic-Related Predictors
Bibliographic record
Abstract
The current study investigates the peritraumatic distress of Chinese residents living in Canada and identifies the associated sociodemographic and pandemic-related predictors during the initial phases of the Coronavirus Disease 2019 (COVID-19) pandemic lockdown (i.e., from April 2020 to June 2020). A final sample of 457 valid participants aged 18 or older completed an online survey in which peritraumatic distress was assessed with the COVID-19 Peritraumatic Distress Index (CPDI). The results showed 32.76% of the sample was in the mild to moderate range (i.e., 28–51) and 5.03% in the severe range (i.e., 52 to higher) for peritraumatic distress. The hierarchical regression models on the continuous CPDI score identified life satisfaction as a consistent protector for the CPDI (absolute values of βs = −1.21 to −0.49, ps < 0.001). After controlling for life satisfaction, the following sociodemographic risk factors were identified: being middle-aged, being employed (relative to retired people/students), living in Ontario (rather than elsewhere), and a poor health status. Furthermore, the following pandemic-related risk factors were identified: a higher self-contraction worry, more of a COVID-19 information authenticity concern, a higher future infection rate prediction, and a higher personal health hygiene appraisal. The results of our study shed light on cognitive, experiential, behavioural, and sociodemographic factors associated with peritraumatic distress for Chinese residents living in Canada during the early outbreak stage of the pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".