GenetiKit: a randomized controlled trial to enhance delivery of genetics services by family physicians
Bibliographic record
Abstract
BACKGROUND: Patients look to their family physicians (FPs) for credible information and guidance in making informed choices about genetic testing. FPs are challenged by lack of knowledge and the rapid pace of genetic discovery. There is an urgent need for effective interventions to facilitate integration of genetics into family medicine. OBJECTIVE: To determine if a multi-faceted knowledge translation intervention would improve skills, including referral decisions, confidence in core genetics competencies and knowledge. METHODS: Randomized controlled trial involving FPs in four communities in Ontario, Canada (two urban and two rural). The intervention consisted of an interactive educational workshop, portfolio of practical clinical genetics tools and knowledge service called Gene Messenger. Outcome measures included appropriate genetics referral decisions in response to 10 breast cancer scenarios, decisional difficulty, self-reported confidence in 11 genetics core competencies, 3 knowledge questions and evaluation of intervention components 6 months afterwards. RESULTS: Among the one hundred and twenty-five FPs randomized, 80 (64%) completed the study (33 control, 47 intervention). Intervention FPs had significantly higher appropriate referral decision scores [6.4/10 [95% confidence interval (CI) 5.8-6.9] control, 7.8/10 (95% CI 7.4-8.2) intervention] and overall self-reported confidence on core genetics competencies [37.9/55 (95% CI 35.1-40.7) control, 47.0/55 (95% CI 44.9-49.2) intervention]. Over 90% of FPs wanted to continue receiving Gene Messengers and would recommend them to colleagues. No significant differences were found in decisional difficulty or knowledge. CONCLUSIONS: This study demonstrated that a complex educational intervention was able to significantly improve practice intent for clinical genetics scenarios found in primary care, as well as confidence in genetics skills.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".