Mental health issues and needs of LGBTQ+ asylum seekers, refugee claimants and refugees in Toronto, Canada
Bibliographic record
Abstract
LGBTQ+ people experience mental health challenges due to their minoritized status, systemic inequities and structural disparities. For LGBTQ+ asylum seekers, refugee claimants and refugees the impact on their mental health can be compounding. This study, which featured a series of focus groups with LGBTQ+ asylum seekers, refugee claimants and refugees in Toronto, Canada, was part of a larger international study ‘Envisioning Global LGBT Human Rights’ that looked at colonising effects on LGBTQ people in the Commonwealth. The migration process, – often forced due to persecution in their country of origin based on sexual orientation or gender identity and expression – produced traumatic experiences involving life-changing decisions, accessing information and resources, cultural shifts, conceptualisation of identities, and navigating the refugees claims process. The specialised experiences of LGBTQ+ asylum seekers, refugee claimants and refugees can have a deleterious effect on their mental health that a critical psychology perspective can address clinically by recognising the particularised needs of this population and systemically by addressing the structural inequities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".