Creating a toolkit with stakeholders for leveraging tablet computers to support person-centred dementia care in hospitals
Bibliographic record
Abstract
Introduction People with dementia may refuse care because they feel overwhelmed by an unfamiliar environment. Everyday technology such as tablets have the potential to support person-centred dementia care in hospitals. Aims We aimed to identify barriers and enabling factors in order to develop a toolkit to support the use of tablets in engaging individual and group activities, especially to play family videos, for hospitalized older people with dementia. Methods A participatory action research approach was employed. We facilitated staff focus groups and conducted interviews with stakeholders. A toolkit was developed based on participants’ perspectives on how to support successful adoption. Results Our analysis identified two enabling factors: users’ engagement in developing a toolkit for support and adapting implementation to meet local needs. Barriers included staff and family inexperience, mechanical instability of hardware, issues around privacy and data access, technology use and personalization of messages. The toolkit includes short videos, a brochure for family caregivers, and a pocket card for staff. Discussion and implications: Staff, family and patients start with varying levels of experience with the use of tablets, making education and support vitally important to implementation. Health organizations should involve staff, patients, and families to find practical solutions.
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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 | 0.031 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".