Using Music Technology Creatively to Enrich Later-Life: A Literature Review
Bibliographic record
Abstract
Background: A growing body of evidence has demonstrated significant social and emotional benefits of music-making amongst senior citizens. However, several as-yet unresolved age-related barriers to ‘musicking’ have been identified. Positioned within the emergent field of gerontechnology, concerned with the interface between aging and technology research, this review of literature thus explores the potential for music technologies to function as a vehicle for creative musical opportunities in later-life. Methods: ERIC, PsychInfo and Web of Science databases were searched, focusing on the intersection between music, technology and ageing. The criteria for inclusion were that the paper should: 1) be in English; 2) report empirical research involving the use of music technologies intended to support receptive (listening, interpreting, reflecting) or active (playing, creating, performing) engagement with music amongst older persons, defined as being aged 60 years or above (United Nations, 2017); 3) be published as a peer reviewed journal article. Results: Of 144 papers screened, eighteen papers were retained. Ten studies focused on using technology to support musicking in the form of listening, reflecting and interpreting. Just five studies explored the utility of technology in promoting singing or playing instruments, while a further three were focused on music and movement. Conclusions: Overall, the literature reviewed suggests that older people, even those with complex needs, are capable of, and interested in using music technologies to access and create personally meaningful music. The limited research that does exist points to multiple and significant benefits that may be derived from receptive or active musicking supported by a range of music technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".