Key challenges in simulated patient programs: An international comparative case study
Bibliographic record
Abstract
BACKGROUND: The literature on simulated or standardized patient (SP) methodology is expanding. However, at the level of the program, there are several gaps in the literature. We seek to fill this gap through documenting experiences from four programs in Australia, Canada, Switzerland and the United Kingdom. We focused on challenges in SP methodology, faculty, organisational structure and quality assurance. METHODS: We used a multiple case study method with cross-case synthesis. Over eighteen months during a series of informal and formal interactions (focused meetings and conference presentations) we documented key characteristics of programs and drew on secondary document sources. RESULTS: Although programs shared challenges in SP methodology they also experienced differences. Key challenges common to programs included systematic quality assurance and the opportunity for research. There were differences in the terminology used to describe SPs, in their recruitment and training. Other differences reflected local conditions and demands in organisational structure, funding relationships with the host institution and national trends, especially in assessments. CONCLUSION: This international case study reveals similarities and differences in SP methodology. Programs were highly contextualised and have emerged in response to local, institutional, profession/discipline and national conditions. Broader trends in healthcare education have also influenced development. Each of the programs experienced challenges in the same themes but the nature of the challenges often varied widely.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".