What Are the Self-Assessed Training Needs of Early Career Professionals in Addiction Medicine? A BEME Focused Review
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Background: Substance use disorders represent a significant social and economic burden globally. Accurate diagnosis and treatment by early career professionals in addiction medicine (ECPAM) falls short, in part, due to a lack of training programmes targeting this career stage. Prior research has highlighted the need to assess the specific training needs of ECPAM. Therefore, this focused review assessed self-reported training needs of ECPAM. Methods: Medical and medical education databases (Medline, EMBASE, CINAHL, ERIC, PSYCHInfo, BEI, and AEI) were searched to June 2018 for studies reporting self-reported training needs of ECPAM (trained at most five years before assessment occurred). Retrieved citations were screened for eligibility; two independent researchers reviewed included studies, assessed quality and extracted data. Experts reviewed study findings. Results: Of 1364 identified records, three cross-sectional studies were included, originating from China, USA and England. All studies surveyed ECPAM using self-reported questionnaires, with one study including face-to-face interviews. Participants included residents, physicians and social workers. All studies had a low risk of bias, and reported a wide range of training needs including rehabilitation, relapse prevention, buprenorphine treatment and risk assessment. Conclusions: There is little evidence for and substantial heterogeneity of training needs of ECPAM found in this review, particularly at the level of skills and knowledge. Study quality varies greatly. ECPAM training needs assessments are 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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".