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Record W3113755243 · doi:10.21203/rs.3.rs-29065/v1

MusicCohort: Cross-sectional feasibility study of an assessment protocol for student musicians

2020· preprint· en· W3113755243 on OpenAlexaffabout
Julius Bruder, Nikolaus Ballenberger, Bethany Villas, Charlotte Haugan, Kimiko McKenzie, Zalak Patel, Amynah Mevawala, Christoff Zalpour, Christine Guptill

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsProtocol (science)Cross-sectional studyComputer scienceMedicineStatisticsMathematics

Abstract

fetched live from OpenAlex

<title>Abstract</title> This study examined the feasibility of employing an assessment battery developed in Germany, investigating mental and physical health of university music students, in Canada. Using a cross sectional design, first-semester music and control students were recruited at two university campuses. Eligibility criteria were: 1) first-semester, full-time undergraduate music major (case) or in another university program (control), 2) over the age of 16. Exclusion criteria were: 1) diagnosis of neurological, orthopaedic or psychological condition, 2) diagnosis of infection or systemic disease, 3) regular consumption of medication for pain or mental health diagnosis, 4) varsity athlete, 5) for controls only, enrollment in music courses where a grade is assigned for music performance (e.g. studio lessons). Both groups completed questionnaires and physical testing, including range of motion, core strength and pressure pain threshold. Data for 19 music and 50 control students were analysed. Musician participants demonstrated tendencies towards poorer mental and physical health. This German protocol is feasible in a Canadian university setting. Canadian music students demonstrate similar mental and physical health outcomes to those in the literature and in the parent study. The results of this feasibility study should be confirmed in a larger study.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.227
GPT teacher head0.563
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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