Improving the LGBTQ2S+ cultural competency of healthcare trainees: Advancing health professional education.
Bibliographic record
Abstract
BACKGROUND: Lesbian, Gay, Bisexual, Trans, Queer, and Two-spirit (LGBTQ2S+) populations experience worse health outcomes compared to age-matched heterosexual and cisgender peers. Health professionals' deficient knowledge and negative attitudes can contribute to these inequities. Healthcare trainees report insufficient LGBTQS2+ cultural competence training. METHODS: In this prospective, mixed-methods pre-post design, Atlantic Canadian health students were tested on knowledge, attitudes and self-reported behaviours towards LGBTQ2S+ populations in healthcare settings. Assessment included psychometric measurements and clinical cases involving normative and non-normative fictional patients. Participants were randomised to intervention or control groups. The intervention consisted of three training sessions lead by LGBTQ2S+ experts and elders from the community. The control group continued with usual training. Full assessment was repeated after training. We also held focus group discussions with students and faculty. RESULTS: The intervention group significantly improved attitudes toward and knowledge of LGBTQ2S+ populations and changed relevant aspects of their performance in the simulated clinical situations. Focus groups identified key gaps in current local training. CONCLUSIONS: Integrating specific training related to LGBTQ2S+ health within health professions programs is an important step toward improving these populations' accessibility to a competent, exhaustive and nurturing healthcare. Additional research on innovative means to expand and broaden the scope of our training is warranted.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".