Educating medical trainees on medication reconciliation: a systematic review
Bibliographic record
Abstract
BACKGROUND: Effective medication reconciliation is critical in reducing the risk of preventable adverse drug events. Medical trainees are often responsible for medication reconciliation on admission, transfer and discharge of the most vulnerable patients; therefore, it is important that trainees are educated on this aspect of quality care. METHODS: We conducted a systematic review using MEDLINE and EMBASE databases to identify education initiatives targeted at improving trainee skill and knowledge in carrying out medication reconciliation. Studies published in English or French between July 1980 and July 2013, where the primary focus of the article was the role of medical trainees in conducting medication reconciliation, and where trainee-specific data was reported, were included. Included articles must have reported trainee-specific data. Given the anticipated heterogeneity and array of outcomes, we were unable to employ a specific tool in assessing the risk of bias across studies. RESULTS: Seven studies met pre-specified eligibility criteria, indicating the lack of published education initiatives targeted towards improving trainee knowledge and experience. Four described an education intervention targeted towards students completing internal medicine clerkship, while the remaining 3 were implemented among residents. Although no two interventions were the same, 5 out of 7 included an experiential component. CONCLUSIONS: Varying success was achieved with medication reconciliation education interventions. While some noted improved competence and/or confidence amongst trainees, namely undergraduate medical students, others noted little effect resulting from the intervention.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".