Athlete and coach-led education that teaches about abuse: an overview of education theory and design considerations
Bibliographic record
Abstract
Research shows that athletes across levels and sports have been subjected to maltreatment with non-sexualised forms such as psychological abuse and neglect found to be the most common. With the normalisation of many of these forms of abuse occurring in sports, researchers have called for the ‘safeguarding’ of athletes to focus on prevention through evidence-based education. Yet evidence-based education that teaches about abuse remains limited in the research literature. Further, an examination of educational theory, design considerations and the implications of such applications when applied to learning contexts in sport remains scarce. This paper is the first generated from a project where an online athlete-and coach-led abuse education program was designed, implemented, and evaluated with the purpose of teaching children through to adults (coaches, athletes) about non-sexualised types of abuse, along with the effects of such maltreatment. This paper provides an overview of the educational theory and design considerations, namely Ivor Goodson and Scherto Gill’s narrative pedagogy and the use of culturally responsive and culturally relevant content, with challenges and possibilities of these applications outlined. Recommendations are then made, based on facilitator and participant feedback which may assist sporting organisations and child protection agencies worldwide when designing, developing, revising, or implementing their own education programs to teach about abuse.
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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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".