Measuring engagement with music: development of an informant-report questionnaire
Bibliographic record
Abstract
OBJECTIVES: This study describes the development of the Music Engagement Questionnaire (MusEQ), a 35-item scale to measure engagement with music in daily life. Music has implications for well-being and for therapy, notably for individuals living with dementia. A number of excellent scales or questionnaires are now available to measure music engagement. Unlike these scales, the MusEQ may be completed by either the participant or an informant. METHOD: Study 1 drew on a community-based sample of 391 participants. Exploratory factor analysis revealed six interpretable factors, which formed the basis for construction of six subscales. Study 2 applied the MusEQ to a group of participants with Alzheimer's disease (AD; n = 16) as well as a group of neurotypical older adults (OA; n = 16). Informants completed the MusEQ, and the OA group also completed the self-report version of the MusEQ. Both groups had an interview in which they described the place music had in their lives. These interviews were scored by three independent raters. RESULTS: The MusEQ showed excellent internal consistency. Five of the factor-derived subscales showed good or excellent internal consistency. MusEQ scores were moderately correlated with a global rating of 'musicality' and with music education. There was strong agreement between self-report and informant-report data. MusEQ scores showed a significant positive relationship to independent ratings of music engagement. CONCLUSION: The MusEQ provides a meaningful and reliable option for measuring music engagement among participants who are unable to complete a self-report questionnaire.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".