Stress experiences among older Chinese immigrants in Canada during the COVID-19 pandemic: A qualitative study
Bibliographic record
Abstract
This study investigates the stress experiences and coping strategies among older Chinese immigrants in Canada during the COVID-19 pandemic. Data was collected through focus group interviews and analyzed using the Transactional Model of Stress and Coping (TMSC). The results showed that participants (N=25; 14 female, 11 male; age 65 and above) faced three main sources of stress during the pandemic: personal life disruption (e.g., hindered family reunions and financial pressure), emotional distress (e.g., fear, uncertainty, and distress), and structural obstacles (e.g., travel restrictions and anti-Chinese discrimination). In addition, participants reported generally three types of coping styles: cognitive coping (e.g., maintaining a positive mindset and redefining risks), behavioural coping (e.g., adhering to the pandemic prevention regulations and maintaining daily activities), and social coping (e.g., participating in online social activities and establishing mutual assistance networks). The results call for culturally sensitive policies and measures in future public health crises to address the specific stressors and coping resources of older immigrants.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".