COVID-19 Peritraumatic Distress and Loneliness in Chinese Residents in North America: The Role of Contraction Worry
Bibliographic record
Abstract
The current study examined the association of COVID-19 contraction worry for self and for family members with COVID-19 peritraumatic distress and loneliness in Chinese residents in North America. A sample of 943 Chinese residents (immigrants, citizens, visitors, and international students) in North America completed a cross-sectional online survey during the second wave of the COVID-19 pandemic (between January and February 2021). Univariate analysis of variance (ANOVA) models identified possible sociodemographic variables that were included in the subsequent hierarchical regression models. According to the hierarchical regression models, self-contraction worry was significantly associated with both COVID-19 peritraumatic distress (B = −4.340, p < 0.001) and loneliness (B = −0.771, p = 0.006) after controlling for related sociodemographic covariates; however, family-contraction worry was not significantly associated with the outcome variables. Additionally, poorer health status and experienced discrimination significantly predicted higher COVID-19 peritraumatic distress, whereas poorer health status and perceived discrimination significantly predicted increased loneliness. The results highlighted the detrimental impacts of self-contraction worry on peritraumatic distress and loneliness during the COVID-19 pandemic in Chinese residents in North America.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".