Teaching Primary Care Genetics: A Randomized Controlled Trial Comparison.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Given the increasing discussions of the impact of genetic medicine within family medicine, it is important to determine the most effective way of teaching this material to family medicine residents (FMRs). The objective of this study was to evaluate and compare the impact of three methods of delivering primary care genetic content to FMRs. METHODS: Curriculum materials and assessment tools were created to teach and evaluate knowledge, skills, and attitudes around four core competencies in primary care genetics, with a focus on hereditary colorectal cancer (CRC). Participants were randomly allocated to four learning conditions: (1) no intervention (control), (2) web-based module outlining genetic concepts applied to CRC, (3) live presentation of the web-based material, (4) live presentation and subsequent standardized patient (SP) encounter. Three months later, all participants completed a written knowledge test, attitude survey, and a standardized patient-based performance assessment. RESULTS: Sixty FMRs completed the study. All three educational interventions resulted in significantly improved outcome measures in knowledge and skills but not attitudes, compared to control. There was no significant difference in outcomes between intervention groups. CONCLUSION: FMRs acquired knowledge and improved skills in genetic medicine with three educational methods. Resources such as faculty expertise in genetic medicine and cost should guide decisions on curricular development for this rapidly expanding field. This may be especially relevant for programs with distributed teaching sites.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".