Medical education in substance‐related disorders: components and outcome
Bibliographic record
Abstract
AIMS: To analyze the process of acquisition by physicians of a body of knowledge and skills in the management of substance abuse. DESIGN: A comprehensive search of English-speaking literature was conducted over 20 years. Articles assessing the outcome of educational strategies in undergraduate, graduate and continuing medical education were examined to determine the targeted sample, the educational strategies involved and the outcomes assessed. FINDINGS: Nine studies in undergraduate education, 11 in graduate and 11 in continuing education met the inclusion criteria. They were generally difficult to compare in design, strategy and outcome analysis. Cognitive knowledge and behavioral skills appear to be easier to obtain compared to more complex attitudinal shifts. CONCLUSIONS: There is growing consensus in the selection of a combined didactic and interactive educational strategy but few empirical data as to the more cost-effective learning interventions. Training must be reinforced at regular intervals. While the expanding panoply of interventions available to physicians should enhance the perceptions of role legitimacy and treatment optimism, cohort studies across levels of education, specialty groups and across-substance and other addictive behaviors are required to determine cost-effective educational strategies.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".