Core Addiction Medicine Competencies for Doctors: An International Consultation on Training
Bibliographic record
Abstract
BACKGROUND: Despite the high prevalence of substance use disorders, associated comorbidities, and the evidence base upon which to base clinical practice, most health systems have not invested in standardized training of health care providers in addiction medicine. As a result, people with substance use disorders often receive inadequate care, at the cost of quality of life and enormous direct health care costs and indirect societal costs. Therefore, this study was undertaken to assess the views of international scholars, representing different countries, on the core set of addiction medicine competencies that need to be covered in medical education. METHODS: A total of 13 members of the International Society of 20 Addiction Medicine (ISAM), from 12 different countries (37% response rate), were interviewed over Skype, e-mail survey, or in person at the annual conference. Content analysis was used to analyze interview transcripts, using constant comparison methodology. RESULTS: We identified recommendations related to the core set of the addiction medicine competencies at 3 educational levels: (i) undergraduate, (ii) postgraduate, and (iii) continued medical education (CME). The participants described broad ideas, such as knowledge/skills/attitudes towards addiction to be obtained at undergraduate level, or knowledge of addiction treatment to be acquired at graduate level, as well as specific recommendations, including the need to tailor curriculum to national settings and different specialties. CONCLUSIONS: Although it is unclear whether a global curriculum is needed, a consensus on a core set of principles for progression of knowledge, attitudes, and skills in addiction medicine to be developed at each educational level amongst medical graduates would likely have substantial value.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".