Integrating trans health knowledge through instructional design: preparing learners for a continent – not an island – of primary care with trans people
Bibliographic record
Abstract
In recent years the need to teach primary care providers to better care for transgender and non-binary (trans) patients has garnered significant scholarly and public attention. The alarming why motivating this surge in trans health primary care education has already been firmly established and needs no further comment. Instead, we offer new perspectives on how to do trans health primary care education. From treasured ‘trans 101ʹ educational interventions to trans health ‘clinical pearls’, the prevailing model used to teach primary care learners represents time-limited cultural competency-based education, which we argue creates an isolated education ‘island’. In rethinking this approach, we present an introduction to the concepts of knowledge integration and the transfer of learning and apply them to show how trans health knowledge and skills should be structured within existing curricula to support effective learning and application. These instructional design considerations have yet to be extensively explored when teaching primary care learners trans health content and may be critical to building pedagogy that ultimately improves healthcare delivery. We conclude that trans health – and trans patients themselves – must not be treated as an isolated education island of knowledge and practice. Rather, it is the responsibility of educators to design instruction that encourages learners to integrate this knowledge with foundational principles of primary care; building bridges across a continent of primary care practice landscapes in turn.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".