Bibliographic record
Abstract
AMERICAN AUTHOR and organizational behavior expert Margaret Wheatley reminds us that professional communities grow in meaningful ways when they discover common values and priorities — a process that can only occur when there is a willingness to ask many questions and be open to new perspectives. In this issue of JMR, we offer three articles that reflect the spirit of such discovery, starting with “Saskatchewan Physicians’ Opinions of their Personalized Prescribing Profiles” (page 7). Authors from Canada studied how regulators perceive physician prescribing of controlled substances and physicians’ reflections on their own prescribing practices — drawing interesting conclusions that could decrease the tensions that sometimes arise around the process of monitoring controlled-substance prescribing. Reflection on one’s prescribing practice can be hard, especially when presented by regulatory bodies. But it can also be a good thing — leading to enlightenment, more appropriate prescribing and enhanced patient safety. In “Public Knowledge and Beliefs Regarding Licensure, Certification and Medical Education of Physician Assistants” (page 26), authors from the National Commission on Certification of Physician Assistants take a serious look at how the physician assistant (PA) profession is perceived by the public. As a relatively new medical profession, PA numbers are small and patients have limited knowledge of how they are trained and credentialed. The profession’s inquiry into patients’ perspectives will help as it works to enhance public understanding and inform decisions regarding regulation. In “Strategies to Enhance Boards of Medicine Responses to Medical Error” (page 17), we learn, through stakeholder interviews, that reinventing state medical boards’ review process is urgent. The traditional review of physicians in isolation is obsolete. Health care, increasingly provided by teams in large health care systems, often leaves practitioners reported to a medical board as victims of system-induced errors. The article helps us reflect on the need for collaboration between health care systems and regulators to identify good physicians, distinguishing them from ones who truly have knowledge or skills deficits. Improved strategies for regulators are needed to address these issues, which bring unintended consequences for practitioners and for patients.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.358 | 0.229 |
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".