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Record W4307329514 · doi:10.1177/01937235221134610

Exploring Stakeholders’ Interpretations of Safe Sport

2022· article· en· W4307329514 on OpenAlexaff
Joseph Gurgis, Gretchen Kerr, Anthony Battaglia

Bibliographic record

VenueJournal of Sport and Social Issues · 2022
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSafeguardingAthletesSport psychologyPsychologyGrounded theoryContext (archaeology)Public relationsSport managementConceptual frameworkApplied psychologyEngineering ethicsSocial psychologyQualitative researchSociologyPolitical scienceEngineeringMedicineSocial science

Abstract

fetched live from OpenAlex

In response to numerous highly publicized cases of athlete maltreatment, sport organizations have developed prevention and intervention strategies under the umbrella term of Safe Sport; however, confusion exists about what it does and does not encompass. To better understand what Safe Sport encompasses, this study sought to develop a conceptual framework of Safe Sport, informed by the perspectives of various stakeholders in sport. Using a social constructivist grounded theory approach, semi-structured interviews were conducted with forty-one participants, including athletes, coaches, sport administrators, and researchers. The results are interpreted to suggest that participants’ understanding of Safe Sport are informed by three overarching themes: environmental and physical safety, relational safety, and optimising sport, all of which are viewed as continuously evolving relative to the ever-changing context of sport and broader society. Recommendations are made to optimise sport experiences and thus prevent physical and psychological harms through a safeguarding approach that prioritizes the promotion of human rights.

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)0.000
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 score0.246
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.137
GPT teacher head0.334
Teacher spread0.197 · 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

Citations29
Published2022
Admission routes1
Has abstractyes

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