Evaluation of physician assistant students' perceived preparedness in providing health care to people who may identify as Lesbian, Gay, Bisexual, and Transgender
Bibliographic record
Abstract
People who identify as lesbian, gay, bisexual, and transgender (LGBT) face barriers to accessing appropriate, non-discriminatory culturally-safe health care. An important strategy to address the disparities experienced by the LGBT population is to ensure preparedness of students graduating from health care professions. Canadian Physician Assistant students’ level of preparedness in caring for LGBT+ patients is unknown. This study used an online survey distributed to students and alumni of the Master of Physician Assistant Studies at the University of Manitoba to characterize the LGBT+-related health curriculum, and to determine Physician Assistant students’ self-reported preparedness in providing care to patients who identify as LGBT+. We also conducted an interview with a curriculum developer to further characterize the curriculum and to confirm survey findings. Of 34 survey participants, 32 were included in the final analysis. Most students/alumni rated the LGBT+-related curriculum as “fair” or worse. The topics that students and alumni felt most prepared to address where HIV, sexually transmitted infections, alcohol use, tobacco and other drug use, safe sex and gender identity. They felt least prepared addressing sex reassignment surgery, transitioning, adolescent health, disorders of sex development, and body image. Finally, by using our findings as a needs assessment, we proposed recommendations for inclusion of LGBT+-related health content in the Master of Physician Assistant Studies program at the University of Manitoba.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".