Music Therapy Assessment for Older Adults: Descriptive Mixed-Methods Study
Bibliographic record
Abstract
Background: The purpose of this inquiry was to test the new ‘Music Therapy Assessment for Older Adults’ (MTAOA) tool in Canada and the United States, and to establish its content and predictive utility. Methods: A pilot study using an explanatory descriptive methods design was chosen; n = 18 music therapists completed an online survey about their experiences in administering the assessment and 50% (n = 9) were invited for a follow-up interview. Results: The results indicated that the MTAOA was a beneficial assessment tool that contained relevant domains (89%) to develop a music therapy treatment plan; 89% of music therapists also noted they would continue to use and recommend the MTAOA. The data produced beneficial information that were used to revise the assessment form to ensure inclusive language and reduce any potential inherent or unconscious biases. Conclusions: Future research is needed to assess the utility of the revised MTAOA in other global regions where music therapists work with older adults.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".