MG-111 Clinical genetics education: Building foundations using e-modules for paediatric residents
Bibliographic record
Abstract
Objectives Clinical Genetics is rapidly evolving so it is essential that future physicians are equipped to practice in a genetics literate world. The goal of this project is to provide non-genetics trainees with an accessible resource to enhance their genetics education and to complement traditional teaching modalities. We are undertaking a pilot study of Paediatric residents’ genetics knowledge and perceptions before and after completing the e-modules. Design/methods We have created two e-modules highlighting common situations encountered in practice, a positive newborn screen and developmental disability/autism. The e-modules lead students through gathering information, interpreting findings, and management strategies. Basic genetic concepts, indications and limitations of testing are highlighted. The modules are designed for trainees prior to exposure to Clinical Genetics, to provide a foundation to build upon with clinical experience. The modules are available through the Queen’s School of Medicine technology platform. A questionnaire assessing knowledge and comfort with genetics will be administered before, immediately after, and 6 months after the modules. Focus groups will be conducted to determine residents’ perception of the modules and thematically analysed through a lens of constructivist grounded theory. Results/conclusions The e-modules have been designed and are in the process of being implemented. By sharing our preliminary experience we hope to expand genetics education opportunities for non-genetics residents.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".