Health Education Literacy and Mobility for Musicians: a global approach - Part II
Bibliographic record
Abstract
Funded by a grant from the Worldwide Universities Network Research Development Fund and generous contributions by partner WUN and WUN+ institutions, this project aimed to build a global research network to address the need for both health education mobility and improved health literacy for musicians world-wide. Music participation is widely acknowledged to have health benefits across the lifespan and to diminish cultural divides. Paradoxically, despite its global popularity and community benefits, the act of making music involves highly repetitive actions under psychologically demanding conditions. Research indicates this may result in a high injury risk for musicians, irrespective of age, musical style, genre or cultural background. It is essential for health promotion to become embedded in the education of musicians to improve health outcomes over their lifespan. This project created an interdisciplinary, multicultural research network focused on developing innovative, sustainable, effective and accessible approaches to musicians’ health education in higher education settings. The project built on existing international collaborations between Australian and Canadian researchers, adding experienced researchers from the Netherlands, New Zealand and South Africa, to form a global perspective on this important public health issue. All team members have substantial track records in health education and research for musicians, yet they have different but complementary expertise and skill sets. Team members met in two intensive collaborative workshops at WUN universities in 2018 to design a large-scale, international research program investigating provision of health education literacy and mobility for musicians, embedding health education into music teaching, and translational applications of an existing online learning resource for musicians’ health. The Musicians' Health Literacy Consortium was formed in this workshop, which marked the continuation of collaborative work on the project and focused on developing research projects to enact the research agenda outlined in Part I. This included: 1) developing a musician-specific health literacy assessment tool; 2) designing a multi-centre cohort study on delivery of musicians' health education online; and 3) developing approaches to embed health education into teaching practice for musicians in higher education using online delivery.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".