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Record W2157307880 · doi:10.1123/jcsp.6.3.293

Sport Concussion Education and Prevention

2012· article· en· W2157307880 on OpenAlexaff
Charles H. Tator

Bibliographic record

VenueJournal of Clinical Sport Psychology · 2012
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsConcussionRecreationPsychologyMedical educationInjury preventionMedicineApplied psychologyPhysical therapyPoison controlMedical emergencyPolitical science

Abstract

fetched live from OpenAlex

There has been a remarkable increase in the past 10 years in the awareness of concussion in the sports and recreation communities. Just as sport participants, their families, coaches, trainers, and sports organizations now know more about concussions, health care professionals are also better prepared to diagnose and manage concussions. As has been stated in the formal articles in this special issue on sport-related concussion, education about concussion is one of the most important aspects of concussion prevention, with the others being data collection, program evaluation, improved engineering, and introduction and enforcement of rules. Unfortunately, the incidence of concussion appears to be rising in many sports and thus, additional sports-specific strategies are required to reduce the incidence, short-term effects, and long term consequences of concussion. Enhanced educational strategies are required to ensure that individual participants, sports organizations, and health care professionals recognize concussions and manage them proficiently according to internationally recognized guidelines. Therefore, this paper serves as a “brief report” on a few important aspects of concussion education and prevention.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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)0.0010.000

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 head0.226
GPT teacher head0.589
Teacher spread0.363 · 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

Citations46
Published2012
Admission routes1
Has abstractyes

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