Survey study on Hong Kong residents recently arrived in Canada (second wave) : Preliminary report
Bibliographic record
Abstract
To further understand how Hong Kong residents who recently arrived or returned to Canada experience their settlement and integration process and how this process may have influenced their health and mental health conditions, we conducted a second wave of online surveys between September 8 and October 10, 2024. A total of 636 respondents completed the survey. As reported in this survey, this group of respondents are primarily middle-aged, highly educated, and fluent in English, consistent with the targeted population of the Canadian government’s special public policy, the Permanent Residence Pathways for Hong Kong Residents. The findings indicate that most respondents experienced difficulties searching for jobs that met their expectations, securing affordable housing, and accessing health care services; their settlement process was largely smooth. Many respondents established and maintained a relatively active social connection with families and friends in Canada and Hong Kong, who have been their significant social support and help. So far, their social circle has tended to be confined to other people from Hong Kong. Most respondents also demonstrated “healthy immigrant effects,” as most reported no major health and mental health concerns. Among all the stressors, over half of the respondents stated that their status and work problems were their significant sources of stress. These problems are interrelated and may be worsened due to the slowdown of their permanent resident application process. Indeed, nearly half of the respondents were unsure if they would stay in Canada for good. When encountering health and mental health issues, most did not receive formal support. They relied mainly on the support from their informal network, showcasing their impressive adaptability in navigating the challenges of settlement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".