SY13-1-3 * INTERNATIONAL ADDICTION MEDICINE: EDUCATIONAL AND TRAINING EFFORTS
Bibliographic record
Abstract
Promoting training in Addiction Medicine worldwide has resulted in a number of efforts, two of which will be the topic of this presentation. A. The international meeting on International Addiction Medicine Training (Nijmegen) has the following aims: – Sharing knowledge and experience in the field across various educational stages; – Sharing ideas about the undergraduate and postgraduate curricula concerning knowledge, skills and competencies; – Determine whether and where international standardization is possible. Highlights and recommendations will be reported. B. A parallel effort has been the drafting of an International Textbook. The Education and Training section has nine chapters. A number of conclusions emerge from this Section. The initiatives described are uniformly recent ones and are at various stages of development. Until recently, there was little international awareness of each other's national efforts and it is hoped that the Section will promote more international collaboration and support and may even be a catalyst for long distance learning. We still lack a clear picture of undergraduate education at various medical schools as in many countries each design its own. This is important because almost every medical doctor will be confronted with addicted patients. A well designed curriculum will presumably help to destigmatize addicted patients but will also bring to the fore that addiction medicine can be an interesting field for future doctors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.308 | 0.105 |
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".