Virtual patients with substance use disorders in healthcare professional education: a scoping review
Bibliographic record
Abstract
Background and objective: Virtual patient simulations are cost-effective methods for training health professionals. Yet, this teaching method is rarely used with clinicians who work or plan on working with people with substance use disorders. This scoping review summarizes the current state of the literature concerning virtual substance use disorder patient simulations in health professionals' training and provides suggestions for future directions. Methods: Online databases were searched for peer-reviewed articles published between January 2010 and June 2024. Results: Twelve studies were included. The development, administration, and evaluation of performance of the simulations are diverse. Most simulations aim to develop screening, brief interventions or referring skill, they target a variety of health professionals' disciplines and report positive learning outcomes. Virtual simulations have good acceptance rates from learners. Conclusions: Enhancing the diversity of clinical skills and patient populations portrayed in simulations, alongside adherence to best practices in simulation development and implementation is suggested to optimize training outcomes in this critical area of healthcare education.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".