USING IPADS TO PROMOTE PATIENT SAFETY AND REDUCE STAFF INJURIES IN DEMENTIA CARE
Bibliographic record
Abstract
Background: The changes associated with dementia can lead to mood alterations and behavioral and psychological symptoms of dementia (BPSD). BPSD are common, affecting up to 90% of persons with dementia over the course of their illness. Not only do BPSD cause distress for the patients with dementia, but also place hospital staff at risk for injury.This project examines whether using an iPad to play a video purposively created for the patient by his or her family may contribute to preventing and reducing BPSD of patients with dementia. Methods: We used a single case study design and mixed methods. With an ABAB withdrawal design, a patient was observed in four phases, A1 (baseline, no intervention), B1 (intervention), A2 (withdrawal, no intervention) and B2 (intervention again). Also, we conducted staff interviews and video analysis to investigate contextual factors and staff experiences. Results: Our preliminary findings support positive effects of the intervention. Staff described the benefits and barriers of integrating iPad into everyday care activities on the hospital unit. Conclusions: This research contributes to the knowledge base of using technology (iPad) as a non-pharmacological intervention in dementia care. The iPad has great potential to be a safe, easy and low cost solution for supporting patient safety and reduction of staff injuries in clinical settings.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".