Music and Dementia: Exploring Protective Factors for Cognitive Function
Bibliographic record
Abstract
Abstract Ample research suggests that musical interventions have the potential to boost social connection, engender positive emotions, and potentially buffer against depression in people with dementia (PwD). Here, our focus concerns expanding the present body of knowledge by quantifying the benefits of a music-based nonpharmacological intervention. The Voices in Motion (ViM) choir is an intergenerational sociocognitive lifestyle intervention designed to support caregivers and PwD. Over the course of 18 months, the well-being of PwD and caregiver dyads (N = 32; mean age = 79.6 years; 53% female) were rigorously assessed using an intensive repeated measures design. This project set out to determine whether positive change in the social dimensions of health (i.e., social connection [SC] and psychological well-being [WB]) ameliorates depression in PwD. Multilevel modeling was employed to examine longitudinal change within and between individuals. SC significantly predicted intraindividual change (□20 = -0.48, p =.03), with a predictive trend for between person differences (□00 = -0.58, p =.08). On occasions when PwD reported more SC, relative to their own baseline, they also reported fewer depressive symptoms. The effect associated with WB was significant at the between-person level (□00 = -0.18, p =.01). Our analysis suggests that a lifestyle intervention targeting psychological health and wellbeing may also contribute to the depressive signs and symptoms in PwD. As the current health care system is forced to adapt to social distancing and constant precautionary measures, it is crucial to understand and potentially harness the protective effects of modifiable lifestyle factors.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".