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Record W3199372426 · doi:10.1177/08862605211045096

Prevalence of Maltreatment Among Canadian National Team Athletes

2021· article· en· W3199372426 on OpenAlexaffabout
Erin Willson, Gretchen Kerr, Ashley Stirling, Stephanie Buono

Bibliographic record

VenueJournal of Interpersonal Violence · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDoping in Sports
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAthletesHarmNeglectPsychologySuicide preventionPoison controlInjury preventionMedicinePsychiatryClinical psychologyMedical emergencySocial psychologyPhysical therapy

Abstract

fetched live from OpenAlex

This study assessed the prevalence of maltreatment experienced by Canadian National Team athletes. In total, 995 athletes participated in this study, including current athletes and athletes who had retired in the past 10 years. An anonymous online survey was administered, consisting of questions about experiences of psychological, physical, and sexual harm, and neglect, as well as questions about identity characteristics, when the harm was experienced, and who perpetrated the harm. Neglect and psychological harm were most frequently reported, followed by sexual harm and physical harm. Female athletes reported significantly more experiences of all forms of harm. Retired athletes reported significantly more neglect and physical harm. Athletes reportedly experienced more harmful behaviors during their time on the national team than before joining a national team. Coaches were the most common perpetrators of all harms except for sexual harm, which was most frequently perpetrated by peers. This study highlighted the prevalence with which Canadian National Team athletes reportedly experience harmful behaviors in sport, suggesting the need for preventative and intervention initiatives.

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)0.004
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 score0.021
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.016
GPT teacher head0.293
Teacher spread0.276 · 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

Citations113
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Interpersonal ViolenceSame topicDoping in SportsFrench-language works237,207