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Managing Agitated Behaviour in People with Alzheimer's Disease: The Role of Live Music

2011· article· en· W1988172707 on OpenAlexfundno aff
Elissa Cox, Madeleine Nowak, Petra Buettner

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2011
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersAlzheimer Society
KeywordsIntervention (counseling)Exploratory researchPsychologyMusic therapyViolinAudiologyClinical psychologyMedicinePhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.049
GPT teacher head0.318
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2011
Admission routes1
Has abstractyes

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