Trauma-informed abuse education in sport: engaging athlete abuse survivors as educators and facilitating a community of care
Bibliographic record
Abstract
The need to focus on abuse prevention in sport has been prioritized due to widespread athlete maltreatment occurring across many sports, levels and regions. One recommendation to prevent athlete maltreatment is through evidence-based education. Despite this recommendation, surprisingly little has been done in this area. A recently proposed idea is that athlete abuse survivors could collaborate with sport organizations in maltreatment prevention initiatives (e.g. speaking invitations, sharing their stories, education initiatives). However, researchers have warned that when collaborations between survivors and sport organizations occur, and trauma-informed practices are not implemented in a way that engages everyone safely, re-traumatisation may result. The present study is one of the first to explore how abuse survivors can be placed at the forefront of education initiatives that are underpinned by international trauma-informed principles. Two abuse survivors from swimming and rowing facilitated education programs that taught about abuse with other athletes in their sports. Evidence-based trauma-informed practices were embedded throughout the educative process to prevent unintended further harms, in turn creating a ‘community of care’. Embedding trauma-informed practices were imperative given the lived experiences of the survivors, and the possibility the education recipients (i.e. athletes) could also be victims of maltreatment. The ‘what’, ‘why’ and ‘how’ of trauma-informed practices are outlined to show the application, and value of this process to not only maltreatment prevention initiatives, but to other levels of sport (e.g. when abuse is reported, selection policy, coach pedagogy).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".