Long-term outcomes of the "Genetics in Primary Care" faculty development initiative.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Between October 2000 and April 2001, 79 primary care physicians (PCPs) and 21 genetics professionals from 20 teaching medical universities across the United States participated in the Genetics in Primary Care (GPC) project (a national faculty development initiative for PCPs with teaching responsibilities). In 2004--2005, follow-up site visits and phone interviews were done to determine whether participation in the GPC faculty development program has had lasting effects on participants' teaching and clinical practices. METHODS: Site visits were performed at nine sites and individual phone interviews at remaining sites. The same questionnaire was used in both settings. Content analysis of responses was performed. RESULTS: Follow-up achieved responses at 19/20 sites, for a site-level response rate of 95%. All respondents reported having made changes to their formal and informal teaching practices. The majority of respondents (86% of phone interviews) also reported changes to their clinical practice, including an increased awareness of genetics in clinical situations and more appropriate referral patterns. All would recommend similar projects to colleagues, but some (32% of phone interviews) would advise assuring that certain conditions are present (eg, protected time, resources). CONCLUSION: GPC has had lasting effects on its participants' teaching and clinical practices.
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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 | 0.011 | 0.018 |
| 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 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".