“Ah, it's best not to mention that here:” Experiences of LGBTQ+ health professionals in (heteronormative) workplaces in Canada
Bibliographic record
Abstract
Introduction: Despite human rights protections for lesbian, gay, bisexual, transgender, and queer (LGBTQ+) people, LGBTQ+ professionals may continue to experience discrimination working in heteronormative systems and spaces. Methods: In this qualitative study 13 health professionals (nurses, occupational therapists, and physicians) from across Canada participated in in-depth qualitative interviews to explore their experiences with work-related microaggressions and heteronormativity. Results: Heterosexist microaggressions from both patients/clients and colleagues were the norm, perpetuating and bolstered by heteronormative workplace and professional cultures. In turn, LGBTQ+ professionals navigated disclosure-decision-making, in power-laden contexts where all options carried potential negative consequences. Discussion: Drawing on the notion of "heteroprofessionalism," we argue that the concept of professional carries encoded within it demands that the occupant of that category be-or present as-heterosexual, an unmarked status that can be readily desexualized. Acknowledging sex and sexuality disrupts "professionalism." We argue that such disruption, indeed dissention, is necessary to open (hetero)professional spaces to LGBTQ+ workers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.021 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".