Integrating addiction medicine training into medical school and residency curricula
Bibliographic record
Abstract
The Affordable Care Act (2010) brings an opportunity to increase the integration of addiction treatment into the health-care system. With the anticipated expansion of addiction care services in primary care, challenges, such as workforce training, can be expected. This presentation discusses challenges and opportunities for addiction medicine training of primary care professionals in Ireland, Canada, and Portland, Oregon. To explore ideas for integrating addiction medicine education into medical school, fellowship, and residency curricula and to consider how implementation barriers can be addressed. The presentation will outline the setup and content of some of the current addiction medicine education in medical schools and residency programs in Ireland, Canada, and Portland, Oregon. Examples from three educational initiatives will be used to generate ideas applicable to each setting and help overcome integration barriers: the St. Paul’s Hospital Goldcorp Addiction Medicine Fellowship ( http://www.addictionmedicinefellowship.org ), the feasibility study on alcohol SBIRT for opioid agonist patients in Ireland (PINTA), and the team-based SBIRT Oregon project ( http://www.sbirtoregon.org ). Scenarios that illustrate implementation strategies, such as educational outreach visits to practitioners—based on overcoming obstacles to change—and facilitators of integrating addiction medicine education into medical school and residency curricula, will be described. The presentation will conclude with an overview of how initiatives in which the authors have been involved may be used to improve addiction medicine education.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 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".