Coming next month in EDUCATING PHYSICIANS
Bibliographic record
Abstract
RESEARCH REPORTS ♦ The Teaching of Cultural Issues in U.S. and Canadian Medical Schools by Glenn Flores, MD, Denise Gee, and Beth Kastner, MPH ♦ Measuring Emotional Intelligence in Medical School Applicants by Robert M. Carrothers, Stanford W. Gregory, Jr., PhD, and Timothy J. Gallagher, PhD ♦ Impact of a Program to Diminish Gender Insensitivity and Sexual Harassment at a Medical School by Charlotte D. Jacobs, MD, Merlynn R. Bergen, PhD, and David Korn, MD ♦ Long-term Outcomes of the New Pathway Program at Harvard Medical School:A Randomized Controlled Trial by Antoinette S. Peters, PhD, Rachel Greenberger-Rosovsky, Charlotte Crowder, MPH, Susan Block, MD, and Gordon T. Moore, MD, MPH ♦ Assessing the Acquisition of Core Clinical Skills through the Use of Serial Standardized Patient Assessments by Michael D. Prislin, MD, Mark Giglio, MD, Ellen M. Lewis, RN, MSN, Sue Ahearn, RN, and StephenRadecki, PhD ♦ A Study of Information Needs and Seeking in Community Medical Education by Keith W. Cogdill, PhD, Charles P. Friedman, PhD, Carol G. Jenkins, MLS, Brynn E. Mays, MSLS, and Michael C. Sharp, MD ESSAYS ♦ Educating Residents about Managed Care: A Partnership between an Academic Medical Center and a Managed Care Organization by Mark Callahan, MD, Oliver Fein, MD, and Michael Stocker, MD ♦ A Course for Teaching Patient-centered Medicine to Family Medicine Residents by Ayala Yeheskel, PhD, Aya Biderman, MD, Jeffrey M. Borkan, MD, PhD, and Joseph Herman, MD ♦ A Theory-based Faculty Development Program for Clinician–Educators by Mariana G. Hewson, PhD ♦ In Progress:73 Peer-reviewed Reports on Innovative Approaches to Medical Education Editor: M. Brownell Anderson Titles and contents may change. For information about articles scheduled to appear in the May 2000 issue, see page 322. Current, upcoming, and archived Tables of Contents are available at Academic Medicine's Web site .
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.173 | 0.074 |
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".