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Record W2268783866 · doi:10.21091/mppa.2015.1004

Risk Factors for Injuries in Competitive Irish Dancers Enrolled in Dance Schools in Calgary, Canada

2015· article· en· W2268783866 on OpenAlexafffundabout
Mercedes Eustergerling, Carolyn A. Emery

Bibliographic record

VenueMedical Problems of Performing Artists · 2015
Typearticle
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsDanceIrishMedicinePhysical therapyEliteDemographyOdds ratioIncidence (geometry)ArtSociologyVisual artsInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Irish dancing has become a popular activity following international exposure to touring dance companies. Previous studies have reported high injury incidence rates in dancers. The objective of this study was to examine risk factors for injuries in competitive Irish dancers in Calgary, Canada. DESIGN: This is a cross-sectional study. Competitive dancers over 12 years of age in Calgary, Canada, were eligible to participate. METHODS: A pen-and-paper survey was administered to gather information on demographics, risk factors for injury, and injuries in the past year. Potential risk factors included age, competitive level, participation in other physical activities, years of participation in Irish dance, and performing a warm-up or cool-down. Incidence proportions (IP) and odds ratios (OR) were estimated. RESULTS: Three of the five accredited dance schools in Calgary participated and a total of 36 questionnaires were completed. Twenty-six dancers (IP=72.2%; 95% CI 54.8-85.8%) reported at least one Irish dance-related injury in the past year. There were 60 injuries reported and the majority (57%) were foot or ankle injuries. Elite level dancers (OR= 6.33; CI 1.27-31.57) and dancers over 18 years of age (OR= 24.43; CI 2.60-229.56) were at greater risk of injury in the past year than non-elite and younger dancers. CONCLUSIONS: Elite dancers and dancers over 18 years of age are at the greatest risk of injury in Irish dance in Calgary, Canada.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.157
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.025
GPT teacher head0.293
Teacher spread0.268 · 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.

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

Citations14
Published2015
Admission routes3
Has abstractyes

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