The Development and Evaluation of Online Podcast Modules as a Toolkit for Teaching Genetics and Genomics Competencies in Post-Graduate Medical Education
Bibliographic record
Abstract
Abstract Background: The lack of comfort with core genetic and genomic competencies among medical trainees and physicians is a barrier to the implementation of precision medicine. To address this, we developed short online modules to promote genetic competencies for use post-graduate medical education. Methods: The educational toolkit was delivered as short online podcasts accompanied by slides. Each core module is approximately 15-20 minutes, and covered basic genetics, genetic testing, counselling and consenting, and interpreting and delivering results. These were supplemented by case-based modules on cancer genetics, prenatal genetics and cardiogenetics. The modules had pre- and post-test multiple choice questions pertaining to genetic and genomic competencies, attitudes towards precision medicine, and perceived competence. Results: Based on the pre- and post-test data, residents reported a discordance between how often they cared for patients with genetic disorders and their level of confidence with core genetic competencies. Post-module evaluations demonstrated a significant increase in confidence in interpreting a microarray, and basic genetics knowledge.Conclusions: Our study demonstrates that podcast modules are an innovative method to promote genetic and genomic competencies to postgraduate medical trainees. Limitations to our study included a small sample size, and further work is needed identify and address barriers to implementation. We suggest that integration at the post-graduate medical education level will be crucial to further promoting the development of precision medicine competencies in medical trainees and physicians.
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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 | 0.025 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".