Managing Agitated Behaviour in People with Alzheimer's Disease: The Role of Live Music
Bibliographic record
Abstract
Background: Agitation due to Alzheimer's disease (AD) presents a challenge to occupational therapists working in the older people care sector. Recently, background music and music therapy have emerged as promising tools in the management of agitation in AD. This exploratory study investigated whether live music could reduce agitated behaviour in people with AD. Method: A quasi-experimental one-group repeated measures design investigated the effect of a live, one-to-one, musical violin intervention on agitated behaviour in people with moderate-severe AD in a residential care facility. Seven participants received the musical intervention on three occasions. Participants were video recorded before, during and after each session. Behaviour was assessed by the investigator and a blinded assessor, using an investigator-modified Cohen-Mansfield Agitation Inventory. Thirty agitated behaviours were examined. Data were analysed using the Friedman test. Results: This intervention reduced agitated behaviour among participants. Significant reductions in pacing/aimless wandering (p = 0.023), performing repetitious mannerisms (p = 0.036) and general restlessness (p = 0.007) were observed. The total number of agitated behaviours decreased significantly (median 5 [range 2–8] behaviours before the intervention to 1 [range 0–4] during and 1 [range 0–5] after the intervention [p = 0.005]). Conclusion: Live music may be an effective strategy to reduce short-term agitated behaviour among people with AD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".