Transgender and gender diverse curriculum in medical imaging programs: a case study
Bibliographic record
Abstract
BACKGROUND: Transgender and gender diverse (TGD) individuals face barriers, including harassment and discrimination, when accessing healthcare services. Medical imaging procedures require personal information to be shared, such as date of last menstrual cycle and/or pregnancy status; some imaging exams are also invasive or intimate in nature. Terminology is based on binary sex creating an inherently cis-heteronormative environment. TGD patients fear being outed and often feel a need to function as educators and advocates for their care. Incorporation of inclusive healthcare curriculum related to TGD populations is an effective means of educating new health providers and promotes safer and more inclusive spaces in healthcare settings. Educators face barriers which hinder the creation and implementation of TGD content. The purpose of this study was to examine the impacts educators are faced with when creating and delivering TGD content in their medical imaging curriculum. METHODS: A case study of medical imaging programs at a Canadian post-secondary institute was undertaken. Data was collected via semi-structured interviews with faculty. Relevant institutional documents such as strategic plans, policies/procedures, websites, and competency profiles were accessed. Framework analysis was used to analyze the data. RESULTS: The study found seven themes that influence the development of TGD curriculum as follows: familiarity and comfort with the curriculum and content change process; collaboration with other healthcare programs; teaching expertise; management of course workload and related. duties; connections to the TGD community; knowledge of required TGD content and existing gaps in curriculum; and access to supports. CONCLUSIONS: Understanding educators' perspectives can lead to an increased sense of empowerment for them to create and incorporate TGD curriculum in the future. Many post- secondary institutions are incorporating an inclusive lens to educational plans; this research can be used in future curriculum design projects. The goal is improved medical imaging experiences for the TGD population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".