Transgender health objectives of training for adult Endocrinology and Metabolism programs: Outcomes of a modified-Delphi study
Bibliographic record
Abstract
BACKGROUND: Transgender people encounter significant barriers when seeking timely, high-quality healthcare, resulting in unmet medical needs with increased rates of diabetes, asthma, chronic obstructive pulmonary disease, and HIV. The paucity of postgraduate medical education to invest in standardization of transgender health training sustains these barriers, leaving physicians feeling unprepared and averse to provide transgender health care. Closing this education gap and improving transgender healthcare necessitates the development of consensus-built transgender health objectives of training (THOOT), particularly in Adult Endocrinology and Metabolism Residency programs. METHODS: We conducted a two-round modified-Delphi process involving a nationally representative panel of experts, including Adult Endocrinology and Metabolism program directors, physician content experts, residents, and transgender community members, to identify THOOT for inclusion in Canadian Endocrinology and Metabolism Residency programs. Participants used a 5-point Likert scale to assess THOOT importance for curricular inclusion, with opportunities for written feedback. Data was collected through Qualtrics and analyzed after each round. FINDINGS: In the first Delphi round, panelists reviewed and rated 81 literature extracted THOOT, achieving consensus on all objectives. Following panelists' feedback, 5 THOOT were added, 9 removed, 34 consolidated into 12 objectives, and 47 were rephrased or retained. In the second Delphi round, panelists assessed 55 THOOT. Consensus was established for 8 THOOT. Program directors' post-Delphi feedback further consolidated objectives to arrive at 4 THOOT for curriculum inclusion. CONCLUSIONS: To our knowledge, this is the first time a consensus-based approach has been used to establish THOOT for any subspecialty postgraduate medicine program across Canada or the United States. Our results lay the foundation towards health equity and social justice in transgender health medical education, offering a blueprint for future innovations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.080 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".