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Understanding the resistance to creating safer ice hockey: essential points for injury prevention

2017· article· en· W2770104201 on OpenAlexafffund
Ryan A Todd, Sophie Soklaridis, Alice K Treen, Shree Bhalerao, Michael D. Cusimano

Bibliographic record

VenueInjury Prevention · 2017
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsCentre for Addiction and Mental HealthSt. Michael's HospitalUniversity of Calgary
FundersCanadian Institutes of Health ResearchPhysicians' Services Incorporated FoundationOntario Neurotrauma Foundation
KeywordsIce hockeySAFERHarmThematic analysisResistance (ecology)PsychologyPoison controlInjury preventionSuicide preventionArgument (complex analysis)Human factors and ergonomicsApplied psychologySocial psychologyComputer securityQualitative researchMedicineSociologyMedical emergencyComputer sciencePhysical medicine and rehabilitationSocial science

Abstract

fetched live from OpenAlex

INTRODUCTION: Despite the known negative health outcomes of concussions in minor level boys' hockey, there has been significant resistance to creating a safer game with less body checking. METHODS: To better understand cultural barriers that prevent making the sport safer for youth and adolescents, semistructured interviews, with 20 ice hockey stakeholders, were conducted and analysed using thematic analysis. RESULTS: Through this analysis, two primary concepts arose from respondents. The first concept is that body checking, despite the harm it can cause, should be done in a respectful sportsmanlike fashion. The second concept is the contradiction that the game of ice hockey is both dynamic and unchangeable. DISCUSSION: Using structural functionalist theory, we propose an argument that the unfortunate perpetuation of violence and body checking in youth ice hockey serves to maintain the social order of the game and its culture. Any strategies aimed at modifying and promoting healthy behaviour in the game should take these concepts into account.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.156
GPT teacher head0.424
Teacher spread0.267 · 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 designTheoretical or conceptual
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

Citations3
Published2017
Admission routes2
Has abstractyes

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