Influence of the #MeToo Movement on Coaches’ Practices and Relations With Athletes
Bibliographic record
Abstract
Inspired by the #MeToo movement, women worldwide are coming forward to publicly share their accounts of sexual violence. These harmful experiences have been reported in a range of domains, including sport. As such, providing safe sport experiences for athletes is at the forefront of current discussions for all stakeholders in the sport environment, particularly coaches. Thus, the purpose of this research was to explore coaches’ perspectives of the #MeToo movement in sport and its influence on coaches’ practices and relationships with athletes. Semistructured interviews were conducted with 12 Canadian coaches, including male (n = 7) and female coaches (n = 5) from a variety of sports and competition environments. The study highlights that coaches expressed strong support for the #MeToo movement, while also noting an associated fear of false accusation. Coaches reflected on how the movement has impacted their coaching practices and relations with athletes and expressed a desire for greater professional development in this area. Implications include a need for greater coach education on safe touch, appropriate boundaries in the coach–athlete relationship, and clarifications regarding the process of investigating athletes’ accusations of sexual violence in order to alleviate coaches’ fears of being falsely accused.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".