Implementing an organizational trauma-informed approach: an urgent priority to protect the well-being of all members in sport from the top down
Bibliographic record
Abstract
Athletes have been central to safe sport agendas and contemporary abuse prevention initiatives. Despite these initiatives, abuse continues to occur across sports and levels, with athletes not the only sport members to be experiencing abuse and its effects. As witnessing or experiencing abuse and/or neglect has been shown to influence the life course causing long-term health impacts such as trauma, a serious abuse legacy may result for many sport members. One way to address the abuse legacy and promote healing/recovery is to be trauma-informed. Yet, such research and initiatives centring on this are in their infancy and limited in application and scope. In this paper, we provide a critical commentary arguing that an organizational trauma-informed approach should be implemented by sporting organizations worldwide to better support all members experiencing the impacts of abuse due to their sport involvement. Our commentary builds on the premise that various sports members (e.g. sport administration workers, coaches, athletes and officials) are experiencing abuse and potentially trauma as a consequence. We show these abuse impacts and trauma using composite vignettes, media examples and sport literature. Another focal point of our commentary centres on what an organizational trauma-informed approach involves and why it is necessary to support safer sport participation for all sports members. These recommendations build on, and extend, recent calls for all participants in sport to be protected and the need to support abuse victims.
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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 | 0.025 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.040 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.021 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 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".