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Record W4399728647 · doi:10.1080/17511321.2024.2361920

Sport-related concussion (SCR) prevention and the nature of sport: possibilities and limitations

2024· article· en· W4399728647 on OpenAlexaboutno aff
Sigmund Loland

Bibliographic record

VenueSport Ethics and Philosophy · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsConcussionPhysical therapySports injuryPsychologyPhysical medicine and rehabilitationInjury preventionMedicineApplied psychologyForensic engineeringEngineeringPoison controlMedical emergency

Abstract

fetched live from OpenAlex

Concussions are traumatic brain injuries that can result from a blow to the head or a jolt to the body. Athletes in many sports are exposed to concussion risks. There is a growing concern in sport and society about sport-related concussions (SRC) and an increasing awareness of the importance of proper diagnosis, treatment, and prevention. A traditional, reactive approach emphasizes sound protocols in cases of suspected SRC. A proactive approach involves identifying various causes of SRC and implementing preventive measures. For example, to reduce SRC prevalence in Canadian youth ice hockey, a ban on body checking has been introduced. When a preventive SRC measure implies a change in the main rules of a sport, it impacts its nature and alters how it is played. This can lead to tensions between SRC prevention and sporting concerns. By employing an extended consequentialist framework that includes normative analyses of the values of sport, I will examine the possibilities and limitations of preventive SRC measures in terms of constitutive rule changes. More specifically, I will propose a consequentialist framework to determine the conditions under which athlete exposure to SRC risks in a given sport can be considered unacceptable, acceptable, and even valuable.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.085
GPT teacher head0.372
Teacher spread0.287 · 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 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
Published2024
Admission routes1
Has abstractyes

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