Off the record: Substance-related disorders in the undergraduate medical curricula in Ireland
Bibliographic record
Abstract
Background: Substance use disorders (SUDs) are a worldwide problem, and have become a major health concern in Ireland particularly. We aimed to determine the extent to which addiction medicine is embedded in the undergraduate medical curriculum in Ireland. Methods: To further investigate the degree to which drug addiction is taught in the Irish medical curriculum an online literature search was performed using Google Scholar, PubMed (from 2009 to present), EMBASE, PsycINFO, CINAHL, and Medline using the keywords “substance-related disorders,” “undergraduate,” “curriculum” and “Ireland.” Additionally, all Irish medical school websites were examined (n = 6), and a Google search and manual searches of conference programs were performed. We used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines to systematically review and discuss findings. Results: A total of zero published studies met the criteria for inclusion in an updated systematic literature search of addiction medicine education in the undergraduate medical curriculum in Ireland. Conclusion: There is currently no documentation of drug addiction teaching sessions in Irish medical schools. Investigations that offer direct contact with medical schools, such as a telephone survey, may provide a more accurate representation of how addiction medicine education is incorporated into the medical school curricula.
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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 | 0.023 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".