“Please let me know when I do not realize it myself”: a qualitative analysis of senior simulated patients’ experiences
Bibliographic record
Abstract
BACKGROUND: Simulated patients (SPs), defined as being over 65 years old, are valuable partners in the training of health professionals related to the care of our aging population. Many senior SPs have been long-time members of SP programs. As SPs age, shifts in their abilities may be observed that, in turn, can affect the overall quality and effectiveness of their participation. It can be challenging and distressing for both the SP educator and the SP to acknowledge these changes and to respond in a compassionate, respectful, and ethical manner that ensures the safe and effective delivery of a simulation for all stakeholders. SP educators are looking for guidance. The aim of this study was to ask SPs from two countries (Switzerland and Canada) to identify the benefits and challenges of working as SPs as they age and to offer strategies to SP educators to accommodate and facilitate their participation. METHOD: A qualitative thematic analysis research design was implemented to address the study aims. A semi-structured approach with a topic guide was used to individually interview 16 SPs (9 in Switzerland; 7 in Canada). Researchers iteratively compared their results until consensus was reached in terms of identifying the themes and subthemes. RESULTS: Three main themes, with corresponding subthemes, were identified: giving and receiving value as senior SPs, recognizing challenges when working as a senior SP, and fostering meaningful involvement for senior SPs. Meaningful involvement focused on creating a sense of security, adapting to changing abilities, acknowledging contributions, and providing opportunities to stay connected to the program. CONCLUSION: This study illustrates the importance of SP educators working with SPs to co-create a safe and effective work environment. Studies like this can serve as a model to provide practical strategies. Through this study, we have learned from senior SPs how we can best support them in their important work.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".