Stepping into the shoes of older people: a scoping review of simulating ageing experiences for healthcare professional students
Bibliographic record
Abstract
BACKGROUND: Ageing simulation suits and equipment give healthcare professional (HCP) students the opportunity to experience what it might feel like to be an older person with age-related disability or illness. Ageing simulation experiences, where students complete activities of daily living (ADL) tasks, aim to reproduce the challenges an older person may face. OBJECTIVES: We undertook a scoping literature review to establish, from the evidence base, what is known about simulating ageing experiences for HCP students and its impact on attitudes towards older patients. METHODS: We applied Arksey and O'Malley's scoping literature review framework to achieve relevant articles. Four databases (MEDLINE, Embase, Web of Science and Cumulative Index to Nursing and Allied Health Literature) were searched resulting in 114 citations. After screening and applying our exclusion criteria, 14 articles were selected for inclusion. RESULTS: Fifty percent of studies were mixed-methods, 35% quasi-experimental, 7% quantitative and 7% qualitative. Two types of simulation experience were identified: (i) workshop based and (ii) ageing games. Simulated impairments included vision, hearing and mobility issues. Most common ADLs simulated were managing medications, finances and functional ability. The majority of studies reported a positive impact on knowledge, attitudes and empathy towards older people. CONCLUSIONS: Teaching in Care of Older People is important in HCP undergraduate curricula and should be a positive experience promoting successful ageing while raising awareness of ageism. Ageing suits have a positive impact on students' attitudes and empathic skills towards older people. Future research should include interprofessional education with HCP students learning together throughout undergraduate training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".