The Gene Messenger Impact Project: An Innovative Genetics Continuing Education Strategy for Primary Care Providers
Bibliographic record
Abstract
INTRODUCTION: Primary care providers (PCP) will need to be integrally involved in the delivery of genomic medicine. The GenetiKit trial demonstrated effectiveness of a knowledge translation intervention on family physicians' (FP) genetics referral decision-making. Most wanted to continue receiving Gene Messengers (GM), evidence-based summaries of new genetic tests with primary care recommendations. Our objective was to determine the value of GMs as a continuing education (CE) strategy in genomic medicine for FPs. METHODS: Using a "push" model, we invited 19,060 members of the College of Family Physicians of Canada to participate. Participants read GMs online, receiving 12 emailed topics over 6 months. Participants completed an online Information Assessment Method questionnaire evaluating GMs on four constructs: cognitive impact, relevance, intended use of information for a patient, and expected health benefits. RESULTS: One thousand four hundred two FPs participated, 55% rated at least one GM. Most (73%) indicated their practice would be improved after reading GMs, with referral to genetics ranked highly. Of those who rated a GM relevant, 94% would apply it to at least one patient and 79% would expect health benefits. This method of CE was found useful for genetics by 88% and 94% wanted to continue receiving GMs. DISCUSSION: FPs found this novel CE strategy, brief individual reflective e-learning, to be valuable for learning about genetics. This method of information delivery may be an especially effective method for CE in genomic medicine where discoveries occur at a rapid pace and lack of knowledge is a barrier to integration of genetic services.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".