Bibliographic record
Abstract
MEDICAL REGULATORS OFTEN FACE CONTROVERSIAL ISSUES that become emotionally charged and politically polarizing. This may result in important regulatory initiatives being paralyzed due to lack of engagement or fear of backlash. Lack of taking regulatory action and avoiding controversy can potentially have long-term consequences.One of the most recent controversial medical issues was physicians on social media platforms denying the existence of COVID-19. This created significant conflict between the physician’s freedom of expression and potential harm to the public. In the article “COVID-denial Invites License Revocation in the UK,” (page 26) Cathal Gallagher and David Reissner discuss the United Kingdom’s regulatory approach and outcome in dealing with this controversial issue.The issue of climate change has become a politically polarizing issue with many healthcare regulators avoiding involvement in this arena. However, the unique role of healthcare regulatory bodies provides an opportunity to play a leadership role in addressing climate change. In the commentary “What Could (or Should) Be the Regulatory Response to the Wicked Problem of Climate Change?” (page 7) Zubin Austin and Aly Háji describe how medical regulators can act in a collaborative and active manner in climate change policy.In an original research article titled “Regulatory Body Perspectives on Complaints and Disciplinary Action Processes for Health Professionals,” (page 14) Ai-Leng Foong-Reichert and co-authors evaluated the Canadian health professional regulatory body approach to complaints and discipline. The authors discovered that there were differences in the complaint process across professions, across provinces, and within a province. This was impacted by differences in provincial health regulatory legislation. This resulted in significant differences in the disciplinary outcomes.
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 | 0.022 |
| 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 | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.251 | 0.139 |
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".