Bibliographic record
Abstract
THE HISTORY AND EVOLUTION of medical regulation around the globe has varied tremendously as humanity has gotten more interconnected by enhanced communication and transportation capabilities. The original processes of medical regulation in Atlantic Canada are varied and inconsistent -in need of modernization. In “Models in Professional Regulation: Choices for Atlantic Canada” Louise Sweatman presents the current state of affairs and possible models for modernization of Atlantic Canadian medical regulation. Will this lead to real change?In “Increasing Access to a Diverse Mental Health Workforce through Emergency Reciprocity Licensure” Ann Nguyen and colleagues review the rapid change and growth of tele-mental health care in New Jersey by the program during the COVID-19 pandemic. The initiative was very successful in diversifying the tele-mental health workforce and better matching patients' preferred language with that of the provider. What's next? Will the insatiable desire to change and grow allow for regulations that adopt such care more permanently?Scott and Olivia Metzger authored “A Shift Left: Revised Regulations for Opioid Prescribing in New Jersey”. The straightforward process they present to modernize controlled substance prescribing and monitoring was recently adopted by their state to decrease risks of developing OUD and OD deaths. Early follow up indicates some success. Will this lead be a process that other states can adopt with similar success?Each article represents the insatiable desire to change and grow—tremendous innovation and motivation for modernization of medical regulation to improve patient access, care, and outcomes. Can we keep up the momentum?
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.208 | 0.087 |
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".