Pride & prejudice: A scoping review of LGBTQ + medical trainee experiences
Bibliographic record
Abstract
PURPOSE: LGBTQ + medical trainees experience significant discrimination. These individuals are stigmatized within a hetero- and cis-normative system, resulting in poorer outcomes in mental health and increased stress regarding career trajectory compared with their hetero- and cis-identifying counterparts. However, literature on the barriers experienced during medical training in this marginalized group is limited to small heterogeneous studies. This scoping review collates and explores prominent themes in existing literature on the personal and professional outcomes of LGBTQ + medical trainees. METHODS: We searched five library databases (SCOPUS, Ovid-Medline, ERIC, PsycINFO and EMBASE) for studies that investigated LGBTQ + medical trainees' academic, personal, or professional outcomes. Screening and full text review were performed in duplicate, and all authors participated in thematic analysis to determine emerging themes, which were iteratively reviewed to consensus. RESULTS: = 0.57). Major themes that emerged in the literature included the prevalence of discrimination and mistreatment faced by LGBTQ + medical trainees from colleagues and superiors, concerns regarding disclosure of sexual and/or gender minority identity, and overall negative impacts on mental health including higher rates of depression, substance use, and suicidal ideation. There was a noted lack of inclusivity in medical education and having an LGBTQ + identity had a large impact on career trajectory. Community with peers and mentors was an important determinant of success and belonging. There was a noteworthy lack of research on intersectionality or positive interventions that improved outcomes for this population. CONCLUSION: This scoping review highlighted important barriers facing LGBTQ + medical trainees, identifying substantial gaps in the existing literature. Research on supportive interventions and predictors of training success is lacking and will be important to foster an inclusive education system. These findings provide critical insights for education leaders and researchers to help create and evaluate inclusive and empowering environments for trainees.
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 | 0.015 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".