A Rapid Review of Designing a Code of Practice for the Music Industry and Mental Health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it