Hospital-Based Addiction Medicine Healthcare Providers: High Demand, Short Supply
Bibliographic record
Abstract
: Substance use disorders account for a significant burden of disease and place an enormous strain on the health care system in the United States and beyond. Despite death tolls climbing, a myriad of evidence-based medications exist to effectively treat many substance use disorders including nicotine, alcohol, and opioid use disorders. To date, hospitals have largely been overlooked as a setting ripe for the delivery of specialized addiction care. This occurs despite a high lifetime prevalence of a substance use disorder (50%) occurring among hospitalized individuals. A potential barrier to this is the lack of addiction medicine training that currently exists in undergraduate and graduate medical education. Consequently, a paucity of existing physicians report feeling competent to adequately screen for, diagnose or treat substance use disorders. Given the prevalence, cost and potentially lethal consequences of substance use disorders, a critical need exists to improve its identification and evidence-based management in hospital settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".