Older adults as simulated participants: a scoping review
Bibliographic record
Abstract
Introduction Simulated participants (SPs) can support development of competencies associated with person-centred care of older adults. There is limited information and understanding about working with older adult SPs and those who support them to create authentic, safe and effective simulations. This review aimed to review simulation literature focused on the care of older adults to explore and summarize what is known about working with older adults as SPs in healthcare providers’ education. Methods This scoping review followed Arksey and O’Malley’s five-stage approach, involving identification, selection, charting, collating, summarizing and reporting on results. Databases searched included MEDLINE, PsychINFO and EBSCO. To capture additional articles, we hand-searched the reference lists of articles selected for inclusion, MedEdPORTAL and Google Scholar. Results From the initial 816 citations identified, 12 articles were in scope. Articles were either research or educational guides. Findings related to general characteristics of articles, description of SPs, conceptualization of SP role/scenario, SP preparation and scenario implementation. Discussion Older adult SPs contribute to healthcare provider training in authentic, engaging, safe and effective ways. This scoping review provides a foundation on which to further develop older adult SP practices by highlighting their well-being and safety (both physical and psychological), promoting ways to increase diversity and inclusion, and emphasizing that the words we use matter. Improved reporting details of articles involving older adult SPs will facilitate the identification of effective 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 | 0.018 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".