The Association of Standardized Patient Educators (ASPE) Standards of Best Practice (SOBP)
Bibliographic record
Abstract
In this paper, we define the Association of Standardized Patient Educators (ASPE) Standards of Best Practice (SOBP) for those working with human role players who interact with learners in a wide range of experiential learning and assessment contexts. These human role players are variously described by such terms as standardized/simulated patients or simulated participants (SP or SPs). ASPE is a global organization whose mission is to share advances in SP-based pedagogy, assessment, research, and scholarship as well as support the professional development of its members. The SOBP are intended to be used in conjunction with the International Nursing Association for Clinical Simulation and Learning (INACSL) Standards of Best Practice: Simulation SM , which address broader simulation practices. We begin by providing a rationale for the creation of the ASPE SOBP, noting that with the increasing use of simulation in healthcare training, it is incumbent on ASPE to establish SOBP that ensure the growth, integrity, and safe application of SP-based educational endeavors. We then describe the three and a half year process through which these standards were developed by a consensus of international experts in the field. Key terms used throughout the document are defined. Five underlying values inform the SOBP: safety, quality, professionalism, accountability, and collaboration. Finally, we describe five domains of best practice: safe work environment; case development; SP training for role portrayal, feedback, and completion of assessment instruments; program management; and professional development. Each domain is divided into principles with accompanying key practices that provide clear and practical guidelines for achieving desired outcomes and creating simulations that are safe for all stakeholders. Failure to follow the ASPE SOBP could compromise the safety of participants and the effectiveness of a simulation session. Care has been taken to make these guidelines precise yet flexible enough to address the diversity of varying contexts of SP practice. As a living document, these SOBP will be reviewed and modified periodically under the direction of the ASPE Standards of Practice Committee as SP methodology grows and adapts to evolving simulation practices.
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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 | 0.141 | 0.255 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".