Measuring engagement with music: development of an informant-report questionnaire
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it