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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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.032
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0040.003
Scholarly communication0.0070.004
Open science0.0040.005
Research integrity0.0270.026
Insufficient payload (model declined to judge)0.0080.008

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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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