Selected Abstracts From the Proceedings of the 2019 Society of Teachers of Family Medicine Conference on Medical Student Education
Bibliographic record
Abstract
From January 31 through February 3, 2019 the Society of Teachers of Family Medicine (STFM) held its 45th annual Conference on Medical Student Education in Jacksonville, Florida. STFM is a collaborative organization composed of members who are dedicated to teaching the discipline to learners of any level. The conference brings together members including physicians, administrators, behavioral scientists, researchers, residents, and students to learn from one another and improve the quality of family medicine education in the United States and Canada. Abstracts for all conference submissions can be found on the STFM website.1 Plenary speakers addressed topics related to health equity (Joanne Rooney, JD, LLM, EdD); discrimination and bias in the medical workplace (Roberto E. Montenegro, MD, PhD); and mentoring in family medicine (Beat Steiner, MD, MPH, STFM President). The STFM Committee on Medical Student Education reviewed the 14 completed educational research projects and selected six exemplary abstracts as the best of the conference. Criteria for inclusion included relevance to medical student education with a focus on family medicine education, study quality, and meaningful conclusions. Five of the abstracts appear in this collection. One has been published in the intervening time.2 Society of Teachers of Family Medicine. 2019 Conference on Medical Student Education Conference Sessions. https://www.stfm.org/conferences/generalinformation/msearchives/. Accessed April 28, 2019. Stumbar SE, Garba NA, Holder C. Let’s talk about sex: the social determinants of sexual and reproductive health for second-year medical students. MedEdPORTAL. 2018;14;10772. https://doi.org/10.15766/mep_2374-8265.10772
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.352 | 0.147 |
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".