Cultivating Agents of Change in Medical Students: Addressing the Overdose Epidemic in the United States Through Enhancing Knowledge of Multimodal Pain Medicine and Increasing Accessibility via Open-Access, Web-Based Medical Education and Technology
Bibliographic record
Abstract
Medical students of today will soon be physician leaders and teachers of tomorrow about important relevant topics including the overdose epidemic and its devastating impact on our society. In the United States, the overdose crisis, including drug opioid-related overdoses, the increasing prevalence of opioid use disorder along with the increasing number of patients with chronic pain are intensifying and call attention for nationwide action. A strong medical educational foundation of the understanding of the relationship between pain and substance use disorder, their treatment including opioid analgesic therapy, multimodal and interdisciplinary care, and long-term management is needed to help cultivate comprehensive knowledge and training to prepare the next generation's frontline practitioners to meet these needs. Yet, traditional educational curricula covering these topics are not standardized in medical schools across the nation in the United States. The advent of web-based medical education and the integration of this technology may offer potential solutions to these challenges. Often found equally effective as in-person learning, web-based medical education through open-access modules and other technologies can help increase accessibility, enhance knowledge of multimodal pain management, safe and effective use of opioid analgesics, and other related topics, and provide flexible and powerful teaching initiatives. Our viewpoint is thus that open-access modules and other technology-integrated teaching initiatives can help deliver excellence in pain education, preparing and empowering medical students-our future agents of change-who will be at the forefront of the overdose epidemic.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".