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Record W4388140894 · doi:10.1080/14647893.2023.2276957

‘I guess you just have to deal with it’: pre-professional ballet dancers’ experiences of pain, injury, and social support

2023· article· en· W4388140894 on OpenAlexaff
Vanessa Paglione, Sarah Kenny, William Bridel, Meghan H. McDonough, Maria das Graças Rodrigues de Araújo

Bibliographic record

VenueResearch in Dance Education · 2023
Typearticle
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsBalletClassical balletBallet dancerThematic analysisPsychologySociocultural evolutionReflexivityDancePhysical therapyMedicineQualitative researchVisual artsSociologyArt

Abstract

fetched live from OpenAlex

Pre-professional ballet dancers are at high risk for musculoskeletal injuries, some of which are not reported and are self-managed by dancers themselves. Understanding the decisions that adolescent dancers make related to their dancing, pain, and injuries is relatively under-researched. Sociocultural contexts and the presence of social support (or not) may influence decisions and experiences of elite adolescent ballet dancers regarding their pain and injury. Therefore, the purpose of this study was to examine pre-professional ballet dancers’ experiences of pain and injury in relation to the culture of ballet and social support. Twelve semi-structured interviews were conducted with pre-professional dancers (11–19 years) enrolled in a summer intensive programme at a ballet school. Using reflexive thematic analysis, five themes were developed: ignoring injuries and making risky choices, expectation of perseverance, influence of dominant constructions of the ballet body, self-assessment and self-management, and the role of various individuals in injury management and prevention. Findings from this research provide greater understanding of the way sociocultural influence and social support shapes pre-professional ballet dancers’ experiences. Strategies to better support the health and wellness of this unique dancer population are recommended.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.141
GPT teacher head0.495
Teacher spread0.353 · 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 designQualitative
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

Citations5
Published2023
Admission routes1
Has abstractyes

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