Music as a determinant of health among First Nations people in Australia: A scoping narrative review
Bibliographic record
Abstract
ISSUE ADDRESSED: While social determinants frameworks are still popular in research about First Nations health in Australia, a growing body of research prefers cultural determinants of health models. Cultural determinants models provide a holistic, strength-based framework to explain connections between health and contextual factors, including the potential role of music and its impact on social and emotional well-being. Given the growing international recognition of links between music, health, and wellbeing through bodies such as the World Health Organisation, this article examines whether and how music practices are acknowledged in First Nations determinants of health literature. METHODS: We conducted a scoping narrative review of literature from five databases: Scopus, PsycInfo, CINAHL, PubMed and ProQuest Central. The search returned 60 articles published since 2017, which we analysed in NVivo for common themes. RESULTS: Music was only explicitly identified as a determinant of health in two studies. Yet, participants in five studies identified music and song as directly impacting their social and emotional well-being. When we broadened our frame of analysis to include other forms of expressive cultural practice, one quarter of included studies empirically acknowledged the role of expressive cultural practice for social and emotional well-being. CONCLUSION: While many recent studies identify the impact of First Nations' expressive practices broadly, they miss important features of First Nations music as a potentially unique cultural, social, political and ecological determinant of health. SO WHAT?: There is an opportunity for future research and health determinant modelling to explicitly examine the role of First Nations music and other creative practices for social and emotional well-being.
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 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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".