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Record W2096319570 · doi:10.1136/bjsm.2009.067751

Psychological predictors of injuries in circus artists: an exploratory study

2010· article· en· W2096319570 on OpenAlexaff
Ian Shrier, Madeleine Hallé

Bibliographic record

VenueBritish Journal of Sports Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsJewish General Hospital
Fundersnot available
KeywordsMedicinePsychologyVisual artsArt

Abstract

fetched live from OpenAlex

OBJECTIVES: To explore the relationship between potential psychological risk factors and injury risk in circus artists. DESIGN: Historical cohort study. SETTING: Cirque du Soleil training programme. PARTICIPANTS: Forty-seven circus artists training to become Cirque du Soleil artists. ASSESSMENT OF RISK FACTORS: Artists completed the validated REST-Q questionnaire (19 domains) during their first 2 weeks of training. MAIN OUTCOME: Injury risk ratio. RESULTS: Of the five a priori exposures of interest, injury, emotional exhaustion, self-efficacy and fatigue were associated with an increase in injury risk (risk ratios between 1.8 and 2.8), but Conflicts/Pressure was not (risk ratio=0.8). Of the several specific psychological aspects that are considered risk factors for injury, low self-efficacy had the strongest relationship. CONCLUSIONS: Most of the strong psychological risk factors for injuries previously identified in athletes also appear to be risk factors in circus artists.

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)0.002
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.330
Teacher spread0.305 · 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

Citations40
Published2010
Admission routes1
Has abstractyes

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