Non-accidental harms (‘abuse’) in athletes with impairment (‘para athletes’): a state-of-the-art review
Bibliographic record
Abstract
OBJECTIVE: Para athletes reap significant health benefits from sport but are vulnerable to non-accidental harms. Little is known about the types and impacts of non-accidental harms Para athletes face. In this literature review, we summarise current knowledge and suggest priorities for future research related to non-accidental harms in Para athletes. DESIGN: Six electronic databases were searched between August and September 2017. 2245 articles were identified in the initial title/abstract review, and 202 records were selected for full-text review following preliminary screening. Two independent examiners evaluated each full text, and eight citations were selected based on inclusion/exclusion criteria. DATA SOURCES: MEDLINE, Embase, PsycInfo, Cumulative Index to Nursing and Allied Health Literature, Scopus and Academic Search Premier. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Inclusion criteria: (A) human participants; (B) written in English; (C) descriptive, cohort and case series, case-control, qualitative, mixed methods studies and all clinical trials; and (D) data pertain to harassment/abuse of youth, recreational, collegiate, national-level and/or elite-level athletes with a physical and/or intellectual impairment. RESULTS: Most studies focused on young, visually impaired athletes and approximately half of all studies described high rates of bullying and its social implications. One study confirmed remarkably high rates of psychological, physical and sexual harms in Para athletes, compared with able-bodied peers. CONCLUSIONS: Bullying in young, visually impaired athletes is described most commonly in the available literature. Due to the limited amount of data, the prevalence of non-accidental harms in Para athletes remains unclear and information on trends over time is similarly unavailable.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".