Bibliographic record
Abstract
Physicians and other health care providers are among the most trusted people in the world. They take on enormous responsibility and have enormous power — often seen as heroes by their patients and their families. With that power and privilege come opportunities for abuse of the same — from compassionately writing excessive prescriptions to sexual violations. The vast majority of physicians are responsible practitioners and upstanding citizens, but as highlighted by the article Preventing Egregious Ethical Violations in Medical Practice (page 23), there is a sub-segment of physicians who abuse their power and harm others in the process. Recommendations for preventing such ethical violations and, when that is not possible, identifying and remediating/reprimanding individuals who commit them early will decrease the incidence and severity/duration of such violations when they occur…Medical regulators and those they regulate are constantly in a sort of tug of war: Those who are regulated want to get credentialed via simpler and faster processes and get to work, while the regulators who oversee them are charged with verifying their training, qualifications and backgrounds in order to protect the public. As noted in the article State-by-State Variations in PA Licensure: A Policy Analysis (page 14), there is great variation in how physician assistants are licensed. In part, this variability may exist because regulations regarding this new specialty were being developed simultaneously in many jurisdictions... Narcotics, benzodiazepines and benzodiazepine-like drugs/Z-drugs in excess and dangerous combinations are the scourge of Canadian and American medical practice. Opioids, Benzodiazepines and Z-Drugs: Alberta Physicians' Attitudes and Opinions upon Receipt of their Personalized Prescribing Profile (page 8) describes how a Canadian prescription drug monitoring system generates individual physician profiles and sends them to each physician. The responses by the physicians to a follow-up survey varied from gratitude and intent to optimize prescribing practices to frustration and confusion. There were requests for more information and for specialty-specific educational activities. These three articles remind us that as we charge into a new year in hot pursuit of protecting the public, once again we are challenged to optimize the way we initially license practitioners, the way we keep them in the loop regarding best practices, and most importantly, how we quickly identify and remediate or reprimand those who stray from ethical practices.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.300 | 0.173 |
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".