Simulation as an innovative approach in dementia caregiver education: A literature review
Bibliographic record
Abstract
Abstract Background Simulation has well been used for training healthcare professional; however, such innovative approach in dementia caregiver education is still at infancy. This study aims to review the application of simulation in the education of dementia family caregiver regarding the simulation modality, skills trained and outcomes. Method Keywords including “caregiv*”, “simulation” and “caregiver education” were used for searching in PubMed, CIHNAL, Medline, Psyinfo and Embase databases. Publications were searched up to 2021 and 7 studies were reviewed. Result Studies were conducted in US, Netherland, Canada and South Korea of sample size ranging from 28 to 264. Study designs included RCT, quasi experimental and non‐experimental. Virtual reality was employed to enhance empathy and competency of caregivers; simulated patient (SP) was employed to enhance communication skill and problem‐solving technique; low‐fidelity simulator (Dementia LiveTM) was employed to enhance empathy and problem solving. Outcomes included improvement in empathy, caregiving competency and decreased use of emotion‐focused and avoidance‐focused coping. None of the studies provided detailed simulation design. Conclusion Simulation as an innovative approach may inform nursing intervention in dementia care. Nurses could incorporate simulation into psychoeducation to enhance dementia family caregivers’ self‐efficacy. Communication skill, problem‐solving technique and behavioral problem can be addressed through therapeutic simulation with the use of SP methodology (Sadvoy et al, 2020). Scenario design should be person‐centered and simulation design should be stringent. Experimental studies are urged to determine the effectiveness.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".