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Record W4404857683 · doi:10.1080/10538712.2024.2434852

Diversity of Profiles Among Adolescent-Athletes Reporting Sexual Violence in Sport

2024· article· en· W4404857683 on OpenAlexafffund
Allyson Gillard, Sophie Labossière, Marie‐Pier Vaillancourt‐Morel, Sylvie Parent

Bibliographic record

VenueJournal of Child Sexual Abuse · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité Laval
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsAthletesDiversity (politics)PsychologySexual abuseClinical psychologySexual violenceSuicide preventionPoison controlMedicinePhysical therapyMedical emergencyCriminologyPolitical science

Abstract

fetched live from OpenAlex

The experience of sexual violence (SV) in sport can vary according to contextual factors such as its form, type of perpetrator, and frequency of acts that might impact the risk factors and outcomes of SV. This study aims to explore the heterogeneity of SV experiences in sport using latent class analysis and to compare the victimization profiles based on personal and sport characteristics as well as on outcomes. A sample of 1357 adolescent-athletes practicing an organized sport who reported SV in sport was included in the study. Four profiles of sexual victimization were identified: (a) SV from authority figure (3.5%), (b) sexual harassment from peers (84.5%), (c) low poly-victimized (6.9%), and (d) moderate poly-victimized (5.2%). Overall, the findings suggest that athletes reporting SV are not a homogenous group but do not clearly distinguish in risk factors and outcomes. Results can be used to better target prevention and intervention strategies.

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)0.000
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 score0.111
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.296
Teacher spread0.266 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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