Development of the Caregiver Confidence using Music Scale
Bibliographic record
Abstract
Music serves as an important tool to improve the health and wellness of individuals in healthcare settings. In times of high caregiver burnout, therapeutic outlets such as music for care receivers and providers are becoming increasingly important. This paper presents the first iteration of the Caregiver Confidence using Music Scale (CCuMS), an assessment tool designed to evaluate caregivers’ readiness to adopt music care. Music care is defined as the informed and intentional use of music by anyone to improve the quality of care. The CCuMS was derived from a hierarchical cluster analysis of the Music Care Training program’s Level 1 post-evaluation survey (Post-MCTL1). Thematic interpretation of the statistical outputs from the cluster analysis was completed, resulting in the first iteration of the CCuMS. Initial validation methods that were feasible with current data were conducted. Specifically, face validity, content validity and convergent validity were calculated using Pearson correlations. The CCuMS shows promise as a measurement tool for use in healthcare settings due to the moderate correlation between the Post-MCTL1 and the CCuMS scale (r=0.524), and the strong correlation between the music care training thematic questionnaire and the CCuMS (r=0.970).
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".