A Rapid Review of Designing a Code of Practice for the Music Industry and Mental Health
Bibliographic record
Abstract
The contemporary music industry is composed of numerous therapeutic resources, small-scale interventions, technological solutions, triage services, and more. The aim of this rapid review is to identify the mental health issues that members of the music industry may experience, and what will inform the development of a music industry “code of practice” for mental health. Research undertaken internationally within the music industry since the 2016 “Can Music Make You Sick?” study has identified that members of the UK music industry community experience negative mental health symptoms notably more than other industries. Negative mental health symptoms within this review can be defined as panic attacks and/or high levels of anxiety and/or depression. A code of practice is a set of written regulations issued by a professional association or an official body that explains how people working in a particular profession should behave. A code of practice helps workers in a particular profession to comply with ethical and health standards. A code of practice within the contemporary music industry would provide a framework within which music industry members can work. Music industry members are defined herein as anyone involved in and/or working in the music industry. It is important to make this clarification, as many of the studies around mental health in the music industry focus on musicians, whereas all roles in the music industry have the potential to struggle with their mental health. The literature identified fundamental problems relating to mental health and the music industry. Help Musicians’ “Can Music Make You Sick?” study from 2016 found that from over 2,000 respondents, 69% of musicians suffered from depression. In Canada, a small study of 50 respondents found that 20% disclosed suicidal thoughts.
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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 | 0.076 | 0.322 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".