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Record W4400011759 · doi:10.1177/00027642241261253

Implementing an Organizational Trauma-Informed Approach to Olympic Sites: An Urgent Priority to Protect Elite Athlete Well-being During Olympic Games Participation

2024· article· en· W4400011759 on OpenAlexaff
Jenny McMahon, Kerry R. McGannon

Bibliographic record

VenueAmerican Behavioral Scientist · 2024
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsLaurentian University
Fundersnot available
KeywordsEliteElite athletesPolitical scienceAthletesPublic relationsPsychologyApplied psychologyMedicinePhysical therapyPolitics

Abstract

fetched live from OpenAlex

Abuse has been acknowledged as an adverse event which leads to trauma and long-term health effects in sport. Given the high rates of abuse occurring in elite sport contexts, many Olympic athletes will not only be subjected to abuse while residing and competing at the Olympic Games but may also experience trauma and its effects. In this article, we build on the calls for a trauma-informed approach in elite sport to outline a rationale for the International Olympic Committee (IOC) to implement an organizational trauma-informed approach to Olympic sites. Such an approach is essential because trauma researchers outside, and inside sport contexts, have outlined that when organizations are not trauma aware, and practices are not trauma-informed, unintended “unsafe” responses may result. To contextualize our rationale for an organizational trauma-informed approach, we provide examples of Olympic athletes’ stories to demonstrate the abuse and trauma they experienced while competing at the Olympic Games. To build on the human right that all athletes participating in the Olympic Games have the right to do so safely and free from harm, we further outline what an organizational trauma-informed approach involves and why it is important to limit re-traumatization risk. We further reflect on how being trauma-informed extends a duty of care to better protect athletes, which should be a responsibility of the IOC.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.355
Teacher spread0.329 · 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.

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 routes1
Has abstractyes

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