What are the training needs of early career professionals in addiction medicine? A BEME scoping review protocol
Bibliographic record
Abstract
Background: Substance use disorders (SUD) represent a significant social and economic burden globally. Accurate diagnosis and treatment by early career professionals in addiction medicine (ECPAM) fails, in part, due to a lack of training programs targeting this career stage. Prior research has highlighted the need to assess the specific training needs of early career professionals working in this area. Aim: To conduct a scoping review of the literature on the self-reported training needs of ECPAM worldwide. Methods: Medical and education databases will be searched for studies reporting perceived training needs of early career professionals (having completed their training within a five year period at the time of assessment) in addiction medicine. Retrieved citations will be screened and full text articles reviewed for eligibility by two independent reviewers. A third reviewer will arbitrate where there was disagreement. Two reviewers will independently extract data from included studies and conduct a quality appraisal assessment. Importance: Overall, the evidence on the training needs from this review will inform efforts to optimise ECPAM education internationally. Training needs assessment of early career professionals working in the field of addiction medicine is a priority.
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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 | 0.094 | 0.086 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.056 | 0.009 |
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".