Bibliographic record
Abstract
While sexual abuse in sport has been prominent in the media, it is important to remember that emotional abuse is the most commonly experienced form of maltreatment in sport. This has been found consistently across genders, sports and countries (Alexander et al., 2011; Brackenridge, 2003; Kirby, Greaves, & Hankvisky, 2000). While the long-term effects of emotional abuse are noted in the child maltreatment literature, they have not been explored in sport specifically. Literature in general child abuse clearly shows that emotional abuse has significant deleterious effects on health and well-being (Kim & Cicchetti, 2010; Mulen et al., 1996). We speculate that emotional abuse in sport may receive less attention from researchers and practitioners because the long-term effects on athletes are less well-known. Therefore, the purpose of this study is to explore the long-term effects of emotionally abusive coaching practices on athletes. Retired Olympians from a variety of sports were interviewed using a semi-structured method. Results indicated that recalling these experiences post-retirement were distressing for the participants and that the coaching practices they perceived to be normal during their careers were appraised post-retirement as being abusive. A variety of effects were reported including both increased and decreased motivation, negative effects on one's sense of self, difficulty trusting others, disordered eating and depressive symptoms although the duration of these effects varied. Implications will be drawn for coaching strategies and athletes' long-term health.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".