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Record W3199467631 · doi:10.1386/jpme_00057_1

Injury prevention education provided during formal drum kit training is associated with lower frequency reporting of playing-related musculoskeletal disorders

2021· article· en· W3199467631 on OpenAlexafffund
Nadia R. Azar

Bibliographic record

VenueJournal of Popular Music Education · 2021
Typearticle
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsUniversity of Windsor
FundersUniversity of Windsor
KeywordsCurriculumMusculoskeletal injuryHuman factors and ergonomicsInjury preventionFormal educationSuicide preventionDrumPsychologyTraining (meteorology)MedicinePhysical therapyOccupational safety and healthPoison controlMedical educationFamily medicineMedical emergencyEngineeringPedagogyAlternative medicine

Abstract

fetched live from OpenAlex

This study explored the relationship between receiving ergonomics/injury prevention education (PrevEd) during formal drum kit training and drummers’ histories of playing-related musculoskeletal disorders (PRMDs) and their engagement in PRMD prevention behaviours. It also explored what they were taught with respect to PrevEd. A mixed-methods analysis of a subset of previously collected survey data ( N = 831) revealed that while 81 per cent of the respondents had completed formal training, only 42 per cent had received PrevEd from their instructors. Respondents who had not received PrevEd were nearly twice as likely to report both lifetime and seven-day histories of PRMD than those who had. They also engaged in warm-ups, cool-downs and exercise significantly less often. Overall, the findings suggest that including PrevEd within drum kit curricula, while reinforcing the importance of regular engagement in optimal PRMD prevention behaviours, warrants further consideration as a primary PRMD prevention strategy.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.020
GPT teacher head0.323
Teacher spread0.303 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2021
Admission routes2
Has abstractyes

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