THE IMPACT OF MUSIC AND MEMORYSM ON RESIDENT MOOD, BEHAVIORS, AND USE OF MEDICATIONS IN NURSING HOMES
Bibliographic record
Abstract
Abstract The numbers of Americans with dementia are projected to increase by 44.8% by 2025. Many of these patients are cared for in nursing homes (NH), 70% of NH residents with dementia have been reported to have significant behavioral or psychiatric symptoms (BPSD) that are often challenging to manage. Historically, the first lines of treatment for BPSD has been antipsychotic medications; however, serious adverse effects have been associated with these drugs. Our main aim was to study the effects of Music and MemorySM (M&M), a personalized music program, on improving behaviors and reducing antipsychotics and other medications in residents with dementia in participating NHs. This 3-year, quasi-experimental, mixed methods study used a cluster, randomized design in three phases. We used the Qualtrics Research Suite to create and disseminate a baseline survey and a 4-part quarterly survey thereafter. The quarterly survey collected select resident MDS data including diagnoses, medication use, pain, falls, mood and behaviors; how M&M was being implemented; resident use of M&M; organization level information. We also downloaded NH 5-Star quality rating quarterly. A total of 265 NH and 4,109 residents participated in the study. We found the odds of antipsychotic use declined by 11%, antianxiety medications by 17%, and antidepressants by 9% per quarter. The odds of residents exhibiting aggressive behaviors declined by 20% per quarter, depressive symptoms by 16% and residents reporting pain by 17%. Our findings indicate that M&M provides substantial benefit to NH residents, particularly in reduction of psychoactive medications and improving mood and behaviors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".