A Systematic Review of the Literature for the Effects of Music on Dementia Patients
Bibliographic record
Abstract
Introduction: There are over 100 different forms of dementia which is a broad term employed to describe symptoms effecting the brain caused by neurological and vascular diseases. It is a neurological disease that results in a decline in cognitive, social and physical disability which currently has no cure. The prevalence is expected to triple by 2050, due to the aging population trends in Canada. It has been estimated that 24.3 million people have dementia, and there are 4.6 million new cases annually, which accounts for one case every 3 seconds globally. Objectives: This systematic review examines the effects of music therapy on behavioral psychological symptoms of dementia and the quality of life. Methods: A systematic literature search was performed in Ovid Medline, CINAHL and PsycINFO from January 2000 until August 2019. A data abstraction template was employed to for data management and organization. We included all peer reviewed, English articles, RCT’s, systematic reviews, meta-analysis, abstract pool and all or some key words. Results: From 968 potentially relevant records, 55 studies were evaluated for eligibility, and 20 met the inclusion criteria. Overall the studies showed a significant impact on mitigating agitation, reducing BPSD and improving memory, cognition, QOL and decreasing anxiety and symptoms associated with depression. Conclusion: The role of musical interventions found in the literature demonstrates the benefits of music therapy on dementia patients due to its positive effects on BPSD, quality of life and other behavioral symptoms.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".