Implementing trauma-informed approaches to coaches’ workplaces in sport to enhance their safety and wellbeing: A critical commentary
Bibliographic record
Abstract
Although safe sport strategies have focused on protecting athletes, coaches’ wellbeing and safety has received less attention. Given recent safe sport directives have been expanded to include all members involved in sport being protected from harm, coaches should not be left out of the discourse. In this critical commentary, we focus on coaches’ potential exposure to adverse events in their workplace, which may lead to them experiencing trauma. To underscore our commentary to include coaches, we draw on composite vignettes and media excerpts focusing on traumatic events experienced by coaches across sports and levels. Examples include coaches being threatened with, or being the recipient/s of violence, witnessing abuse, witnessing traumatic injury or death, and being bullied/cyber bullied, all of which have been linked to trauma. These examples support a case for why trauma-informed work environments should be prioritised by sport organisations to support coach wellbeing and enhance coach safety.
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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 | 0.027 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.024 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 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".