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Record W3033921607 · doi:10.1249/jsr.0000000000000722

Sexual Violence in Sport: American Medical Society for Sports Medicine Position Statement

2020· article· en· W3033921607 on OpenAlexaff
Jennifer Scott Koontz, Margo Mountjoy, Kristin Abbott, Cindy Miller Aron, Kathleen C. Basile, Chad T. Carlson, Cindy J. Chang, Alex B. Diamond, Sheila A. Dugan, Brian Hainline, Stanley A. Herring, Elliot Hopkins, Elizabeth A. Joy, Janet P. Judge, Michele LaBotz, Jason Matuszak, Cody J. McDavis, Rebecca A. Myers, Aurelia Nattiv, Jeffrey L. Tanji, Jessica Wagner, William O. Roberts

Bibliographic record

VenueCurrent Sports Medicine Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPosition statementStatement (logic)Sports medicineMultidisciplinary approachTask forceHuman factors and ergonomicsMedicinePoison controlSuicide preventionPosition (finance)Sexual medicineAthletesTask (project management)Position paperSexual assaultMedical educationFamily medicinePhysical therapyMedical emergencyPsychiatryPolitical scienceSociologySocial scienceLawEngineeringPathology

Abstract

fetched live from OpenAlex

The American Medical Society for Sports Medicine (AMSSM) convened a group of experts to develop a Position Statement addressing the problem of sexual violence in sport. The AMSSM Sexual Violence in Sport Task Force held a series of meetings over 7 months, beginning in July 2019. Following a literature review, the task force used an iterative process and expert consensus to finalize the position statement. The objective of this position statement is to raise awareness of this critical issue among sports medicine physicians and to declare a commitment to engage in collaborative, multidisciplinary solutions to reduce sexual violence in sport.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.025
GPT teacher head0.359
Teacher spread0.334 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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