Deception and informed consent in studies with incognito simulated standardized patients: empirical experiences and a case study from South Africa
Bibliographic record
Abstract
Simulated standardized patients (SPs) are trained individuals who pose incognito as people seeking treatment in a health care setting. With the method’s increasing use and popularity, we propose some standards to adapt the method to contextual considerations of feasibility, and we discuss current issues with the SP method and the experience of consent and ethical research in international SP studies. Since a foundational discussion of the research ethics of the method was published in 2012, a growing number of studies have implemented this method to collect data on the quality of care in a variety of settings around the world. We draw from that experience to provide empirical foundations for a popular approach to ethical approval of such studies in the United States and Canada, which has been to obtain a waiver of informed consent from the health care providers who are the subjects of the research. However, the majority of studies to date have evaluated quality of care outside the U.S., requiring additional ethical consideration when partnering with international institutions. We discuss these considerations in the context of a case study from a completed SP study in South Africa, where informed consent is constitutionally protected.
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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 | 0.097 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| 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".