Factors influencing LGBTQ+ disclosure decision-making by Canadian health professionals: A qualitative study
Bibliographic record
Abstract
Disclosure of LGBTQ+ identities at work may reap benefits, but may also exacerbate harms. Faced with ambiguous outcomes, people engage in complex concealment/disclosure decision-making. For health professionals, in contexts of pervasive heteronormativity where disclosure to patients/clients is deemed to violate professional boundaries, stakes are high. This qualitative study with 13 LGBTQ+ health professionals across Canada used semi-structured interviews to explore factors affecting disclosure decision-making, particularly attending to power structures at multiple levels. Most participants engaged in constant risk-benefit assessment, disclosing strategically to colleagues, rarely to clients/patients. At the individual level they were affected by degree of LGBTQ+ visibility. At the institutional level they were affected by the culture of particular professional fields and practice settings, including type of care and type of patients/clients, as well as colleague interactions. Professional power-held by them, and held by others over them-directly affected disclosures. Finally, intersections of queer identities with other privileged or marginalized identities complicated disclosures. Power relations in the health professions shape LGBTQ+ identity disclosures in complex ways, with unpredictable outcomes. Concepts of professionalism are infused with heteronormativity, serving to regulate the gender and sexual identity expression of queer professionals. Disrupting heteronormativity is essential to forge more open professional cultures.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".