Collaborative framework for working with older simulated participants (SP)
Bibliographic record
Abstract
Introduction As the global population ages, healthcare providers must prepare for the complexities associated with caring for older adults, defined according to the WHO, as being over the age of 60. Simulation-based education in healthcare allows caregivers to practice and master skills and competencies associated with care of older adults. Simulated patients/participants (SP), well people trained to portray other individuals, are an effective choice when training behavioural skills (eg, communication). When working with older SPs, it is important to recognise unique considerations and requirements related to physiological changes, in physical, cognitive and sensory systems associated with normal ageing. Method SP educators from two different countries, with diverse backgrounds and contexts, collaborated through an iterative, consensus-based process to create a framework for working with older SPs. Results A practical three-phase framework with specific strategies was developed that synthesised elements of best practices related to simulation methodology with relevant clinical evidence. Discussion Effective collaboration with older SPs is achievable through investing resources in preparing, training and ensuring their well-being. Through faculty development of healthcare simulation educators, we can ensure that older SPs and simulation communities have the right tools and support to safely and effectively contribute to simulation-based education.
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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 | 0.109 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".