Standardized Patient Practices: Initial Report on the Survey of US and Canadian Medical Schools
Bibliographic record
Abstract
Background: There is currently a lack of information about the ways in which standardized patients (SPs) are used, how programs that facilitate their use are operated, the ways in which SP-based performance assessments are developed, and how assessment quality is assured. This survey research project was undertaken to describe the current practices of programs delivering SP-based instruction and/or assessment.Method: A structured interview of 61 individual SP programs affiliated with the Association of Standardized Patient Educators (ASPE) was conducted over a 7-month period. A web-based data entry system was used by the 11 trained interviewers.Results: The two most common reported uses of SPs were learner performance assessment (88% of respondents) and small-group instruction (84% of respondents). Fifty-four percent of programs hired 51-100 SPs annually and paid an average of $15 and $16 per hour for training time and portraying a case, respectively. The average reported number of permanent program employees, excluding SPs and temporary staff, was 4.8 (sd =3.6). The most frequently reported salary range was $30,001-$45,000.Conclusion: We intend for these preliminary results to inform the medical education community about the functions of SPs and the structures of programs that implement these complex educational endeavors.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".