Feasibility and acceptability of an iPad intervention to support dementia care in the hospital setting
Bibliographic record
Abstract
BACKGROUND: Staying in the hospital can be a very stressful experience for older people with dementia. A familiar face and reassuring voice of a family member or friend can offer a sense of safety and comfort. AIMS: To explore the feasibility and acceptability of using an iPad Simulated Presence Therapy intervention with hospitalized older people with dementia. DESIGN: We used a mixed-method design, incorporated video-ethnographic methods, video-recorded observations, and staff interviews. METHODS: Four people with dementia from an older adult mental health hospital unit in British Columbia, Canada participated in two weeks of iPad Simulated Presence Therapy intervention. The intervention involved the older person watching a one-minute video prepared by their family prior to receiving care. The video included a reassuring, comforting and supportive message to be played to the older adult with dementia while staff perform a specific care task. The care interactions with the iPad intervention were video-recorded. Staff interviews were conducted to elicit perceived enabling factors and barriers to use the iPad intervention in their practice. Using an inductive and deductive approach, we applied a qualitative thematic analysis to identify themes in our data set. RESULTS: We identified four themes: (a) positive responses, (b) person-centred care, (c) video content, and (d) technical skills. CONCLUSION: The iPad delivered Simulated Presence Therapy is an acceptable and feasible means of supporting the care of older people with dementia in the hospital setting. Considerations for future research and clinical practice are presented.
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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".