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Record W4210647345 · doi:10.33607/rmske.v2i25.1128

Static and Dynamic Balance and Injury Prevalence in Snowboard Instructors

2022· article· en· W4210647345 on OpenAlexaboutno aff
Anike Vanagas, Vilma Dudonienė

Bibliographic record

VenueReabilitacijos mokslai slauga kineziterapija ergoterapija · 2022
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsBalance (ability)Dynamic balancePhysical medicine and rehabilitationPsychologyComputer scienceMedicineEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Background. Snowboarding is a quite popular winter sport, though associated with the risk of injury.
 Aim: to determine the relationship between sport injuries and static and dynamic balance in snowboard instructors.
 Methods. The study included snowboard instructors from Ontario, Canada. Questionnaires were given before and after the winter season to obtain injury history. Static balance was evaluated with a Wii Balance Board. Dynamic balance was evaluated using the Y balance test. The results were compared between different genders, age and days on-snow per season.
 Results. Male snowboard instructors had, on average, higher static balance scores than the females. Both male and female scores for testing with eyes closed were significantly lower than with eyes open. The female snowboard instructors had, on average, higher dynamic balance scores than the males. However, for eyes closed testing, female snowboarders’ scores were noticeably better than the males’ scores. Nine of out twenty snowboard instructors had sustained one or more injuries in the past snowboarding season. One female and one male sustained two injuries each, and in total there were ten injuries amongst twenty snowboarders.
 Conclusions. There was no significant difference between prevalence of injury and balance amongst different genders.
 Keyword: snowboarding, sports injuries, static balance, dynamic balance.

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 score0.066
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
Bibliometrics0.0010.001
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 head0.004
GPT teacher head0.244
Teacher spread0.240 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueReabilitacijos mokslai slauga kineziterapija ergoterapijaSame topicWinter Sports Injuries and PerformanceFrench-language works237,207