Using fsQCA to Illuminate Person Attributes of Music Engagement in Alzheimer's Disease
Bibliographic record
Abstract
Preserved engagement with music in Alzheimer's disease (AD) is noteworthy given that such persons lack interest and engagement in the activities of daily life. Because music engagement is associated with increased well-being, illuminating personal attributes that facilitate music engagement is an important step towards utilizing music as a therapeutic tool. Here, we use Fuzzy Set Qualitative Comparative Analysis, a systematic approach to case study series analysis, to explore the role of personal attributes such as musical semantic memories, music perceptual abilities, and overall cognitive status in facilitating music engagement in 15 individuals with a diagnosis of probable AD. Nine different solution terms revealed many different pathways to preserved music engagement in AD. Solutions demonstrated the equifinality of music engagement and the usefulness of the qualitative comparative analysis approach. This article is meant to provide both concrete evidence for the role of different person attributes in music engagement in AD and an illustration of the application of qualitative comparative analysis. We discuss our results using the Comprehensive Process Model as a framework and provide suggestions on how to incorporate qualitative comparative analysis in the research workflow.
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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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".