Feeling ‘not enough’ or ‘too much’: Exploring how LGBTQ+ adults experiencing disability navigate Canadian health contexts
Bibliographic record
Abstract
Disability and LGBTQ+ communities experience healthcare disparities, however, most research has looked at these communities separately which erases the unique health experiences of people who belong to both. This project sought to explore intersections between gender, sexuality and disability within Canadian health contexts through three life-story interviews with seven adults (aged 25-35; 21 interviews total) who identified as LGBTQ+ and experiencing disability. Thematic narrative analysis resulted in interrelated themes associated with axes of self-identification that demonstrated how participants navigated tensions between being perceived as not disabled, trans and/or queer 'enough' or 'too much' within healthcare settings. Participants relayed stories of strategically omitting and/or sharing aspects of their intersectional identities with healthcare providers to receive the care they needed. This study, in demonstrating some of the difficult compromises and decisions LGBTQ+ adults who experience disability navigate to access healthcare, highlights how ableism, cis-heterosexism and racism intertwine to shape medical systems.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.018 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".