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Interpersonal Violence in Belgian Sport Today: Young Athletes Report

2022· preprint· en· W4292641269 on OpenAlexaff
Tine Vertommen, Mieke Decuyper, Sylvie Parent, Aurélie Pankowiak, Mary N. Woessner

Bibliographic record

VenuePreprints.org · 2022
Typepreprint
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAthletesInterpersonal violenceContext (archaeology)PsychologyPsychological interventionNeglectSexual violenceInjury preventionSuicide preventionInterpersonal communicationPoison controlSafeguardingClinical psychologyMedicinePsychiatrySocial psychologyPhysical therapyMedical emergencyGeographyCriminology

Abstract

fetched live from OpenAlex

Initiatives to safeguard athletes from interpersonal violence (IV) are rapidly growing. In Belgium, the knowledge on the magnitude of IV in sport is based on one retrospective prevalence study from 2016 (n=2.043 adults), who participated in organized sport before 18 years. Data on victimization rates in current youth sport populations are lacking. This study aimed to investigate the magnitude of IV in a sample of 769 athletes (13-21 years old), using the Violence Towards Athletes Questionnaire (VTAQ). All types of IV are prevalent in this sample, ranging from 27% (sexual violence) to 79% (psychological violence and neglect). Boys reported significantly more physical violence, while girls reported significantly more sexual violence. IV perpetrated by peer athletes was reported to the same degree as IV perpetrated by a coach (70%), while IV perpetrated by a parent in the context of sport was somewhat less common, but still prevalent (48%). These findings, including factors associated with elevated exposure rates, can serve as a baseline measurement to monitor and evaluate current and future safeguarding interventions in Belgian 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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.085
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.064
GPT teacher head0.362
Teacher spread0.299 · 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 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

Citations7
Published2022
Admission routes1
Has abstractyes

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