P02-102 - Socio-cultural Determinants of Mental Health of Elderly Immigrants: Lessons Learned from Canada
Bibliographic record
Abstract
Introduction The increase of immigrants in Canada has set an example for Western countries on how cultural diversity affects health, mental health, and needs. The mental health of elderly immigrants, however, is often forgotten in service delivery system. Objectives This paper examines the socio-cultural determinants of mental health of elderly immigrants in Canada through synthesizing research findings from various studies. The presentation aims to highlight a few key intervention focuses for addressing mental health of this vulnerable immigrant subgroup. Methods Secondary data analysis was used, using three national surveys and a local community survey, including the Ethnic Diversity Survey, the General Social Survey, the Health and Well Being of Older Chinese in Canada, and the Health and Well Being of Older South Asian in Calgary. Mental health was presented by life satisfaction, self-reported stress, self-reported happiness, self-reported level of trust, sense of belonging, feeling out of place, worrying about hate crime, depressive symptoms, and general mental health measured by SF-36. Results Socio-cultural determinants related to a higher level of social support, a higher level of trust, and better financial resources were found to be correlates of more positive mental health. Racial discrimination, culture related beliefs, and barriers often led to negative mental health outcomes. Conclusions Promoting a better mental health should expand beyond the use of individual interventions and treatments. Socio-cultural determinants have to be addressed through creating a more positive social and structural environment for the elderly 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 | 0.003 | 0.005 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".