1315. Mind the Gap: Medical Trainees Require Training in Hepatitis C, Drug Use and Mental Health to Help Address the Opioid Crisis
Bibliographic record
Abstract
Abstract Background Dramatic increases in acute hepatitis C (HCV) incidence is linked to the opioid epidemic and increased injection drug use. Over 50% of people with HCV also have a mental illness. IDSA/HIVMA calls for the integration of infectious diseases, addiction medicine, and mental health as key to addressing the opioid epidemic. Barriers identified include limited physician education and stigma. This study examined medical trainees’ gaps in training and attitudes toward HCV, drug use, and mental illness. Methods Medical students and residents (N = 98) at a large Canadian University completed questionnaires assessing stigma, attitudes, knowledge, and training related to HCV, drug use, and mental illness. Results Most participants were medical residents (71%). Within-subjects ANOVAs showed that trainees worked with more patients with mental illness (71%) than drug use (55%) or HCV (21%) (P’s < 0.001). Trainees reported less positive experiences with patients with drug use (34%) and HCV (36%) compared with those with mental illness (55%) (p’s < 0.05). They reported that injection drug use (68%), prescription opioids (66%), and heroin use (59%) were the most challenging substance use problems to treat (P < 0.001). They were less satisfied working with patients with drug use (40%) or HCV (40%) than mental illness (59%) (P’s < 0.01). Trainees reported they were more able to help patients with mental illness (83%) than HCV (65%) or drug use (73%) (P’s < 0.01). Only 34% saw HCV treatment as central to their professional role. Their training better prepared them to treat mental illness (58%) than drug use (41%) or HCV (19%) (P’s < 0.001). They were more interested in training in drug use (76%) and mental health (71%) than HCV (62%) (P’s < 0.01). Conclusion Medical trainees report being ill-equipped to treat patients with HCV and drug use (specifically opioids) and are less satisfied with this work. Many report attitudes that may be viewed by patients as stigmatizing. There is a large knowledge gap related to the effectiveness of HCV treatment. Addressing the opioid crisis requires a physician workforce that is prepared to integrate treatment for HCV, drug use, and mental illness. Infectious disease specialists can take a leadership role in building capacity to foster integration. Disclosures All authors: No reported disclosures.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.004 |
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".