Gendered sport environments and their theoretical contributions to women’s ACL injury risk: a scoping review
Bibliographic record
Abstract
Abstract Objective To summarize the current literature that describes the gendered environments of women’s sports which are higher risk for ACL injury, and determine whether the existing literature relates gendered aspects of the sport environment to injury. Design Scoping review. Data sources Electronic search of Medline, Embase, CINAHL, SPORTDiscus, SCOPUS, and Women’s Studies International databases from inception to March 2024. Eligibility criteria Studies were included if at least 50% of the study participants were adult women participating in organized sports with higher risk for ACL injury. Results Of the initially identified 17,148 studies, 854 underwent full text review, and 73 were included in this scoping review. In 19 studies, reference to injury was restricted to one or two direct quotes from an athlete and the other 54 studies had no mention of injury. We identified three repeating patterns describing the gendered sport environments that women athletes encounter. Fifty-five studies described embedded stereotypes that devalue women and women’s sport. Forty-five studies described ways the sport environment reproduces restrictive gender norms for women. Forty-six studies reported that gendered inequities including gendered wage inequality and provision of subpar training facilities were structurally embedded in women’s sport environments. Conclusion Existing literature describes a range of gendered inequities that exist for women in their sport environments; however, there has been no concerted effort to date to link those gendered environmental factors to ACL injury. Such research is needed if we are serious about eliminating the ACL injury rate disparity between women and men. What is already known Historically, gendered disparities in anterior cruciate ligament injury risk have focused on biological explanations Societal gendered norms and expectations of women can greatly affect their experiences and opportunities in sport environments What are the new findings Existing literature describes a range of gendered inequities that exist for women in their sport environments Only a small percentage (∼25%) of studies describing the gendered aspects of women’s sport environments mention any relation to injury There has been no concerted effort to link gendered aspects of sport environments to women’s injury risk and experiences
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".