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Record W4387738405 · doi:10.1080/17430437.2023.2268555

Telling adults about it: children’s experience of disclosing interpersonal violence in community sport

2023· article· en· W4387738405 on OpenAlexaff
Mary N. Woessner, Aurélie Pankowiak, Emma Kavanagh, Sylvie Parent, Tine Vertommen, Rochelle Eime, Ramón Spaaij, Jack Harvey, Alexandra Parker

Bibliographic record

VenueSport in Society · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSafeguardingInterpersonal violenceInterpersonal communicationIntervention (counseling)PsychologyInterpersonal relationshipSuicide preventionSocial psychologyPoison controlMedicineNursingPsychiatry

Abstract

fetched live from OpenAlex

A challenge in safeguarding children from interpersonal violence (IV) in sport is the reliance on self-disclosures and a limited understanding of the frequency, barriers to and process of disclosures of IV. Through a mixed-methods design, combining survey and interviews, we explored the frequencies of childhood disclosures of experiences of IV in Australian community sport as well as who children disclosed to and how the interaction unfolded. Those who experienced peer violence disclosed at the highest frequency (35%), followed by coach (27%) or parent (13%) perpetrated IV. A parent/carer was most often the adult that the child disclosed to. Interviews highlighted how the normalisation of violence influenced all aspects of the disclosure and elements of stress buffering (normalising or rationalising) particularly underpinned the disclosure interaction. Policies and practices should explicitly identify all forms of IV in sport as prohibited conduct; education and intervention initiatives should target parents as first responders to disclosures.

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0040.003
Open science0.0010.006
Research integrity0.0020.004
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 head0.340
Teacher spread0.315 · 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 designQualitative
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

Citations21
Published2023
Admission routes1
Has abstractyes

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