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).
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 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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".