Sociodemographic and Health Determinants of Loneliness in Older Immigrants in Canada: A Cross-Sectional Study
Bibliographic record
Abstract
The COVID pandemic brought to light the pressing issues of social isolation and loneliness for older adults. Immigrant older adults' experience of loneliness is even more exacerbated by factors, such as, language barriers, and the loss of cultural community. Key determinants of loneliness in older immigrants are not clear in the literature. A cross-sectional study was conducted in nine cities across Canada to: describe the experience of emotional, social and overall loneliness; and examine the determinants of loneliness among Punjabi, Mandarin, and Arabic-speaking older immigrants. A total of 647 older immigrants participated in the study. Descriptive statistics were used to describe their experience of loneliness, and multiple regression analysis was performed to examine the determinants of loneliness. Most participants had a post-secondary education, were married, and had been in Canada for about 16 years. On average, the participants reported good physical and mental health, and moderate levels of emotional, social, and overall loneliness. Ethnocultural group, emotional wellbeing, and depression were associated with emotional loneliness. Social loneliness was associated with education, depression, psychological distress, age, and ethnocultural group. Determinants of overall loneliness were age, gender, ethnocultural group, self-rated mental health, emotional wellbeing, depression, and psychological distress. Community based interventions that target these key factors must be designed to address loneliness experienced by older immigrants.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".