Exploring non-gender influences on sport participation among Transgender athletes
Bibliographic record
Abstract
Gender related policies, social attitudes, and environmental factors contribute significantly to Transgender athlete sport drop out (Haghgoo & Law, 2022). Non-gender related factors influencing drop out often impact Transgender individuals at a greater magnitude than their cisgender counterparts because of the complexity of their intersectional identities. The aims of this study were to explore factors unrelated to gender that influence sport drop out among Transgender individuals, and to describe sport participation of those who later returned to sport. Participants (n = 28, Mage = 22.96 years, SD = 2.73) completed an online survey related to their experiences in sport. While 85.7% participants indicated their initial decision to drop out of sport was related to their gender identity, they also indicated work (52.4%) and family commitments (57.1%), finances (57.6%), transportation (42.9%), and health (42.9%) as contributors. Despite these previous challenges, participants were generally satisfied with their current sport participation (M = 5.89, SD = 2.13) and most had returned to participating in a combination of team and individual sports. Of note, some of the sports that participants indicated they were unable to play in the past due to restrictive gender-based policies (e.g., basketball, volleyball, dance) were also identified most frequently as sports participants currently engage in. While findings suggest inclusive sporting environments exist, there is a need to identify qualities of sport environments that support inclusivity and to develop policies and programs supportive of diverse identities. Developing an understanding of the complexity of these identities may encourage continuous sport participation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".