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Record W1968740986 · doi:10.1097/htr.0000000000000018

Exploring Minor Hockey Players' Knowledge and Attitudes Toward Concussion

2014· article· en· W1968740986 on OpenAlexaff
Martin Mrázik, Andrea Perra, Brian L. Brooks, Dhiren Naidu

Bibliographic record

VenueJournal of Head Trauma Rehabilitation · 2014
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsAlberta Children's Hospital
Fundersnot available
KeywordsConcussionMinor (academic)SeriousnessAthletesIce hockeyPsychologyMedicineInjury preventionPhysical therapyPoison controlPhysical medicine and rehabilitationMedical emergencyHumanities

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate minor hockey players' attitudes and knowledge about sport concussions. PARTICIPANTS: Male and female Pee Wee, Bantam, and Midget level players (n = 183) participating in minor hockey and a comparison group of non-hockey players (n = 57). DESIGN: Survey. MAIN MEASURES: Player knowledge and attitudes were evaluated with a standardized questionnaire developed for the purpose of this study. Descriptive statistics including cross-tabulations and proportion comparisons were used to report the data. RESULTS: Players had foundational knowledge about concussions; however, more than half underestimated the prevalence and more than 30% were unaware of return to play protocols. Although nearly all players knew what they "should" do when concussed, 33% did not follow recommendations. Players reported more concern and appreciation of the seriousness of concussion than non-players, but they tended to minimize their vulnerability. The most common and helpful information sources were parents, doctors, and coaches, and therefore knowledge translation efforts should target theses audiences. CONCLUSION: Young athletes continue to demonstrate gaps in their knowledge of concussions. In addition, attitudes toward concussion suggest a developmental trajectory with younger athletes being most likely to ignore current recommended guidelines.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 integrity0.0000.000
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 head0.177
GPT teacher head0.390
Teacher spread0.213 · 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

Citations35
Published2014
Admission routes1
Has abstractyes

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