Supporting Migrant 2SLGBTQIA+ Unpaid Caregivers for Family Members Living with Chronic Illnesses
Bibliographic record
Abstract
The literature details the healthcare needs of migrant people living with chronic illnesses and the consequent economic, social, and healthcare needs of their caregivers. Similarly, some studies have underscored the social and healthcare needs of 2SLGBTQIA+ (two-spirit, lesbian, gay, bisexual, transgender, queer, and intersex individuals, including diverse sexual and gender identities under the "+" symbol) adults living with chronic illnesses and their caregivers. This narrative review presents the context of migrant 2SLGBTQIA+ unpaid caregivers and how their intersecting identities influence their caregiving roles for family members with chronic illnesses. In this article, caregivers are defined as family members or chosen families who provide unpaid support that may last for three months or longer for people living with chronic illnesses. Most studies and policies overlook 2SLGBTQIA+ migrants who are also unpaid caregivers of individuals living with chronic illnesses, leaving them unsupported through discrimination at the intersection of racism, homophobia, transphobia, ageism, and ableism, forcing them to remain vulnerable to increased emotional and physical strain. There is a presence of pervasive systemic barriers, including a lack of training and education among social and healthcare providers, about the needs of migrant 2SLGBTQIA+ unpaid caregivers. Additional challenges stem from inadequate policies and insufficient targeted resources, particularly for caregivers from marginalized racial and ethnic backgrounds. The findings of this study highlight the necessity for a call to action to address these gaps and improve support systems for these highly marginalized communities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".