Advancing Health Equity Among Older Racialized LGBTQ+ Immigrants: Findings of a Scoping Review and Program Scan
Bibliographic record
Abstract
Canada’s population is aging. It is estimated that there are at least 400,000 LGBTQ+ seniors in Canada. Despite the advancement of gay rights in Canada, LGBTQ+ seniors, especially members of racialized and/or immigrant communities, have often been excluded from research and health care planning. Our team conducted a scoping review of existing evidence on the health and wellbeing of racialized and/or immigrant LGBTQ+ seniors. The review shows: There is limited research in Canada. Out of the 14 studies selected, only two were conducted in Canada. The health and wellbeing of racialized and/or immigrant LGBTQ+ seniors are affected by many social factors: household income, education, limited familial and social support, housing, food security, and social exclusion. Many racialized and immigrant LGBTQ+ seniors tend to distrust the health care system due to experiences of stigma and discrimination. At the same time, services are often inaccessible or not available to them. As a result, they may delay treatment and rely on alternative care such as spiritual and natural healing. These factors lead to poor health and mental health outcomes. To advance health equity, it is important to address multiple related factors by: Engaging racialized/immigrant LGBTQ+ seniors to identify their unique needs Providing culturally inclusive and accessible services that affirm their intersecting social identities Promoting their individual and collective resilience Addressing systemic barriers and social isolation Ensuring equitable funding and resources
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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 | 0.034 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".