Teaching health professionals how to tailor gender-affirming medicine protocols: A design thinking project
Bibliographic record
Abstract
BACKGROUND: Content knowledge surrounding transgender (trans) medicine is currently lacking in the formal medical education curricula. Evidence indicates that the main protocols used to assess and refer trans patients for gender-affirming medicine are misunderstood by health professionals, and require flexible adaptation to achieve health equity and patient-centred care. APPROACH: A free online educational tool for gender-affirming medicine, The Path to Patient-Centred Care, was developed to teach learners how to adapt assessment protocols. Resource creation was supported by a knowledge translation grant that endorsed design thinking, a human-centred and solutions-focused framework recommended for use in curriculum development. EVALUATION: The Path to Patient-Centred Care provides learners with information related to key principles of patient-centred care in gender-affirming medicine, including a guide on how to adapt the main assessment protocols to achieve equitable care. The curriculum also includes narratives from trans patients and health professionals that focus on health equity, and a clinical vignette about a complex case, designed to foster critical thinking on medical ethics. Project future directions involve an implementation and evaluation pilot study with a diverse group of continuing professional development medical learners using a mixed-methods program evaluation design. REFLECTION: The use of design thinking to develop this resource exemplifies a novel approach to curriculum development. By using pedagogical strategies that foster critical reflection, this innovative online education tool strives to teach self-directed learners how to provide care that emphasizes trans people's self-determination and autonomy in medical decision-making.
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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 | 0.031 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".