The association of standardized patient educators (ASPE) gynecological teaching associate (GTA) and male urogenital teaching associate (MUTA) standards of best practice
Bibliographic record
Abstract
Gynecological Teaching Associates (GTAs) and Male Urogenital Teaching Associates (MUTAs) instruct healthcare professional learners to perform accurate and respectful breast, speculum, bimanual vaginal, rectal, urogenital, and prostate examinations. During such sessions, the GTA/MUTA uses their own body to instruct while providing real-time feedback. While GTAs/MUTAs fall under the broader umbrella of Standardized Patient methodology, the specificity of their role indicates need for establishment of Standards of Best Practice (SOBP) for GTA/MUTA programs. On behalf of the Association of Standardized Patient Educators (ASPE), the Delphi process was utilized to reach international consensus identifying the Practices that comprise the ASPE GTA/MUTA SOBP. The original ASPE SOBP was used as the foundation for the iterative series of three surveys. Results were presented at the ASPE 2019 conference for additional feedback. Fifteen participants from four countries completed the Delphi process. Four of the original ASPE SOBP Domains were validated for GTA/MUTA programs: Safe Work Environment, Instructional Session Development, Training GTAs/MUTAs, and Program Management. Principles and Practices were shaped, and in some instances created, to best fit the distinct needs of GTA/MUTA programs. The ASPE GTA/MUTA SOBP apply to programs that engage GTAs/MUTAs in formative instructional sessions with learners. Programs that incorporate GTAs/MUTAs in simulation roles or in summative assessment are encouraged to reference the ASPE SOBP in conjunction with this document. The SOBP are aspirational and should be used to shape Practices within the program's local context. The ASPE GTA/MUTA SOBP will continue to evolve as our knowledge-base and practice develop.
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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 | 0.040 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".