Bibliographic record
Abstract
Engaging with music has proven positive impacts on mental health and wellbeing, yet musical interventions are rarely used in healthcare settings.Professor Gilles Comeau, from the University of Ottawa in Canada, hopes to change this.He has established the Music and Mental Health Research Clinic to explore the relationships between music and mental health and to develop ways of integrating music into healthcare services. Harnessing the power of music to improve mental healthMusic for mental health H ave you ever felt happier after listening to music, playing an instrument, singing a song or dancing along to your favourite tunes?Interacting with music can have significant benefits for both your physical and mental health, meaning music can play an important role in your well-being."Participating in musical activities can help cognitive function, reduce the risk of developing mental illnesses and reduce the severity of existing mental health conditions," says Professor Gilles Comeau, Director of the University of Ottawa's Music and Health Research Institute and of the Music and Mental Health Research Clinic at The Royal, a specialised mental healthcare centre.Gilles is convinced of the power of music for improving health and well-being.His mission is to make healthcare-related music participation accessible to all who will most benefit from it.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".