Using 360-degree videos to raise empathy and understanding of dementia
Bibliographic record
Abstract
A lack of dementia understanding can complicate caring for people living with this condition. Caregivers need to connect with people living with dementia on an affective level to better understand their unique needs. 360-degree video is a type of virtual reality that can allow one to experience how it is to live with dementia. This study utilized 360-degree videos filmed from the point of view of people living with dementia to enhance nursing students and practicing nurses’ dementia empathy and understanding. Sixteen participants watched two 360-degree videos and participated in individual in-depth interviews. The cathartic powers of these videos made participants feel isolated, misunderstood, or confused, like those living with dementia. This helped participants reflect on the care they have been providing to patients living with dementia and share how they will enhance their care. Experienced nurses shared insights related to priorities and helpful strategies in providing dementia care. Novice nurses gained a better perspective of how it feels to live with dementia. It is recommended to include these videos as part of orientation, in-service training, and in nursing education curricula. Participants also recommended filming other 360-degree videos portraying scenarios with advanced care strategies useful in dementia care.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".