Simulated Experiential Learning Activity to Empower Paid and Unpaid Caregivers in Dementia Care
Bibliographic record
Abstract
The number of people with dementia is rising worldwide. People with dementia are challenged by the symptoms of their illness, as well by discriminatory attitudes and actions. However, this stigmatization is not only experienced by people with dementia; it is also experienced by the paid and unpaid caregivers who provide care for people with dementia. Education is a key strategy to reduce stigma and improve the quality of life of individuals with dementia. It also has the potential to provide caregivers with meaningful forms of support. A systematic review of the literature demonstrated that the most effective educational intervention to change attitudes and reduce stigma are resources that incorporate an in person contact approach. Dementia Live™ is a simulation tool that places learners “in the shoes” of people with dementia and is used to raise awareness of what it might be like to live with dementia. The targeted population includes students, health care and social care workers, staff from hospitals, longterm care residences, retirement homes, and home care services, as well as friends, volunteers and family caregivers. This educational intervention can serve as a model to develop additional simulation tools to reduce all types of stigma to support a safe learning and work environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".