Towards equity & inclusion: a critical examination of genetic Counselling Education on Intersex Healthcare
Bibliographic record
Abstract
BACKGROUND: Intersex describes a diversity of individuals with variations in sex characteristics (VSC), reflecting underlying differences in reproductive anatomy, hormones, and/or genes and chromosomes. With a shift towards socially-conscious clinical practices, genetic counsellors (GCs) are increasingly needing to provide comprehensive care to individuals with VSC and their families. However, the current quality of training provided to genetic counsellors on intersex health is unclear. METHODS: Qualitative interviews were conducted between Jan-Feb 2021 with 20 current and graduated students of Canadian GC training programs to assess the quality of GC education on intersex health topics. An agency-based model of VSC health as proposed by Crocetti et al. was used to guide the inductive thematic data analysis. RESULTS: Results revealed three key themes: limited discussions on psychosocial considerations when caring for intersex individuals, enthusiasm for integrating more intersex training into the curriculum, and personal initiative in ensuring equity and justice in the care of individuals with VSC. CONCLUSIONS: These findings demonstrate existing knowledge gaps in the GC curriculum, with a need to increase the profession's overall awareness on intersex issues. GC training programs have an opportunity to meet the desires of students while promoting person-centered and validating healthcare for the intersex community.
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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 | 0.169 | 0.298 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.038 | 0.036 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.006 | 0.035 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".