Experiences of local residents who portrayed patients in simulated patient exercises
Bibliographic record
Abstract
With a view toward developing Simulated Patient Exercises (SPE) together with local residents, we aimed to ascertain the role that instructors should play, and the coordination they should undertake to achieve this goal. We interviewed local residents in their 60s and 70s who participated in the participatory SPE program of Tokai University’s Adult Nursing Department. We asked the residents about what they gained from, and how they felt about, their experience in the program, and about the kind of support they need to play their role effectively. From the interview data and qualitative inductive analysis, we derived four themes describing the residents’ experiences: “Self-encouragement and growth”, “hardships as a Simulated Patient”, “efforts to improve the training of simulated patients”, and “wishes for nursing students”. Many of the statements described hardships the residents faced as Simulated Patients, including their nervousness and the burden of learning their role. On the other hand, we also heard many positive statements about how the residents felt encouraged and achieved growth through the experience. For example, in serving as simulated patients, the residents were encouraged by a sense of pride at how they were helping to train nursing students who will in the future help them and their family members, and they felt inspired by the students’ earnestness and their cordial learning attitudes, as well as by the camaraderie with fellow Simulated Patients. These positive statements illustrate the value of local residents participating in the program, and they explain why the residents continued to participate.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".