Recommendations from LGBTQ+ adults for increased inclusion within physical activity: a qualitative content analysis
Bibliographic record
Abstract
For decades, physical activity contexts have been inherently exclusionary toward LGBTQ+ participation through their perpetuation of practices and systems that support sexuality- and gender-based discrimination. Progress toward LGBTQ+ inclusivity within physical activity has been severely limited by a lack of actionable and practical suggestions. The purpose of this study was to garner an extensive account of suggestions for inclusivity from LGBTQ+ adults. Using an online cross-sectional survey, LGBTQ+ adults (N = 766) were asked the following open-ended question, "in what ways do you think physical activity could be altered to be more inclusive of LGBTQ+ participation?" The resulting texts were coded using inductive qualitative content analysis. All coding was subject to critical peer review. Participants' suggestions have been organized and presented under two overarching points of improvement: (a) creation of safe(r) spaces and (b) challenging the gender binary. Participants (n = 558; 72.8%) outlined several components integral to the creation and maintenance of safe(r) spaces such as: (i) LGBTQ+ memberships, (ii) inclusivity training for fitness facility staff, (iii) informative advertisement of LGBTQ+ inclusion, (iv) antidiscrimination policies, and (v) diverse representation. Suggestions for challenging the gender binary (n = 483; 63.1%) called for the creation of single stalls or gender-neutral locker rooms, as well as for the questioning of gender-based stereotypes and binary divisions of gender within physical activity (e.g., using skill level and experience to divide sports teams as opposed to gender). The findings of this study represent a multitude of practical suggestions for LGBTQ+ inclusivity that can be applied to a myriad of physical activity contexts.
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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 | 0.078 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".