Effective engagement of survivors of harassment and abuse in sport in athlete safeguarding initiatives: a review and a conceptual framework
Bibliographic record
Abstract
Sport, as a microcosm of society, is not immune to the abuse of its stakeholders. Attention to abuse in sport has recently become a priority for sport organisations following several high-profile cases of athlete abuse from different sports around the world. Resulting from this increased awareness, many sport organisations have commenced work in the field of athlete safeguarding including the development of policy, educational programmes, reporting pathways, investigation mechanisms and research initiatives. One mechanism adopted by many sport organisations to support their safeguarding efforts is the engagement of survivors of abuse in sport: typically, as guest speakers at conferences or educational events. Unfortunately, many sport organisations do not have the knowledge or trauma-informed expertise to engage survivors safely and effectively; and in doing so, may unintentionally retraumatise the survivor if erroneous methods of engagement are employed. For some survivors, this experience may compound the original harms, and thus it also represents an area of vulnerability for the organising entity. The purpose of this paper is to explore the rationale for partnering with survivors of abuse in sport in safeguarding initiatives and to propose a living conceptual framework to support effective and safe survivor engagement in safeguarding initiatives. We will explore the underpinning scientific background, as well as the 'why', and 'how' of survivor engagement to inform sport organisations, research scientists, policy-makers, conference organisers, safeguarding officers, sport medicine clinicians and survivors themselves.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".