MétaCan
Menu
Back to cohort
Record W1982422265 · doi:10.3390/jcm2020022

Factors Affecting Ankle Support Device Usage in Young Basketball Players

2013· article· en· W1982422265 on OpenAlexafffund
Michael D. Cusimano, Ahmed Faress, Wilson Luong, Khizer Amin, Joanne Eid, Tamer Abdelshaheed, Kelly Russell

Bibliographic record

VenueJournal of Clinical Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineBasketballAnklePhysical medicine and rehabilitationPhysical therapySurgery

Abstract

fetched live from OpenAlex

This cross-sectional study explores factors affecting the decision of basketball players to wear ankle support devices (ASDs). A questionnaire regarding attitudes towards ASD usage was developed based on the Health Belief Model (HBM). The questionnaire assessed HBM perceptions (susceptibility, severity, benefits, and barriers) and modifying factors (demographic, personal history of ankle injury, influence of coach to preventive action) that may affect an athlete's decision to wear ASDs. One hundred forty basketball players competing at the recreational, high school, or university levels completed the questionnaire, with the questionnaires being completed at the basketball gymnasium or at home. It was found that athletes whose coaches enforced ASD use were significantly more likely to wear them (OR: 35.71; 95% CI: 10.01, 127.36), as were athletes who perceived ankle injuries to be severe (OR: 2.77; 95% CI: 1.04, 7.37). Previous injury did not significantly increase the odds of using an ASD. The combined influence of coach enforcement and previous injury had the greatest effect on increasing ASD use. The largest barrier to ASD use was a lack of aesthetic appeal. Strategies aimed at increasing players' willingness to wear ankle protection should be emphasized among coaches and parents as this may increase use of ASDs.

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)0.005
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.131
GPT teacher head0.422
Teacher spread0.291 · 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

Citations7
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Clinical MedicineSame topicFoot and Ankle SurgeryFrench-language works237,207