Bibliographic record
Abstract
THIS ISSUE OF JMR offers a substantial collection of articles on a variety of topics — all intended to help us make progress toward even more effective medical regulation. Our featured authors demonstrate for our readers a diverse array of policies and processes that have potential for optimizing medical regulators’ ongoing mission of public protection. We begin with the topic of electronic prescribing of controlled substances and providing feedback to individual prescribers regarding their habits (page 8). Overdoses on prescription-controlled substances continues to be a serious problem, and authors from Saskatchewan, Canada describe a system in which individual physicians receive personal structured prescribing feedback regarding their electronic prescribing habits in order to achieve quality improvement. We follow that with articles from authors in New Zealand and Australia, describing a major effort by Australian medical regulators to better understand the use of chaperones in physician sexual misconduct cases (page 17). Australian regulators concluded that the disadvantages of chaperones outweigh the advantages, and began a national movement away from their use as a result. Beginning on page 32, we have paired two articles on another topic of importance to regulators: cognitive screening of practicing physicians. Statistics show that our physician workforce is aging, and is beset by high levels of stress and burnout. Two approaches toward screening are offered — one that focuses on screening older physicians and another that suggests a lifelong model, with screening starting much sooner in a physician’s career. Next, we feature an interesting look at responses to an announcement from the Educational Commission for Foreign Medical Graduates (ECFMG) that, starting several years from now, physicians applying for ECFMG Certification must graduate from a medical school that meets its updated accreditation requirements (page 49). The authors compiled online input from those who will be impacted by this change to sense how it is being received. Finally, on page 57, we close with a summary of the FSMB's latest Census of Licensed Physicians in the United States, which is published by JMR every other year and which offers key insights into physician workforce trends. We hope you find value in this very full edition.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.302 | 0.190 |
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".