Self-perceived confidence and competence of medical students in treating patients with diverse sexualities and genders
Bibliographic record
Abstract
Introduction: Insufficient knowledge among health staff about people with diverse sexualities and genders (DSGs) can hinder access to care. This student-initiated project aimed to ascertain medical students’ self-perceived confidence and competence in treating patients with DSGs, identify student satisfaction with DSG curricular content and identify discrimination and/or biases. Methods: In 2022, medical students from an Australian medical program were surveyed using an adapted medical student survey instrument used in the United States and Canada. Results: The response rate of 15% (n = 91) included 70% heterosexual students. Respondents reported more confidence in treating patients with diverse sexualities (68%) compared to patients with diverse genders (41%). They reported greater competence in identifying their own implicit biases and impact on the delivery of DSG care (75%) but were less competent in describing treatment options (17%). Respondents reported having witnessed or experienced DSG discrimination (26%) and heterosexism (38%). Respondents (93%) want increased DSG content in the medical curriculum. Conclusion: To address gaps in DSG health knowledge among clinicians, further DSG teaching in the medical curriculum may inform better future practice and patient care.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".