MG-111 Clinical genetics education: Building foundations using e-modules for paediatric residents
Bibliographic record
Abstract
<h3>Objectives</h3> 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. <h3>Design/methods</h3> 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. <h3>Results/conclusions</h3> 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.
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 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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".