UBC Intramurals: Identifying and Assessing Barriers Limiting Female Participation Rates
Bibliographic record
Abstract
Background: At the University of British Columbia (UBC), there is a decreasing rate of female participation in Intramurals sports. Purpose: To identify key barriers influencing the decline in women’s participation in UBC Intramurals and develop recommendations for UBC Athletics and Recreation to reduce barriers and facilitate female Intramural participation. Methods: A 23-question online survey was created with Qualtrics to collect responses from female UBC students regarding their perceived barriers to participation and feedback for UBC Intramurals. Participants were recruited through survey links posted on Facebook. Qualtrics and Microsoft Excel were used to conduct descriptive and content analyses, respectively. Results: Common barriers identified by female UBC students included self-esteem and the convenience and accessibility of UBC Intramurals. Three main themes for improvement were identified for UBC Athletics and Recreation and included better access to information regarding UBC Intramurals leagues, consistent league schedules and game times, and the creation of additional leagues, such as gender-specific and just for fun leagues. Conclusion: Five recommendations were made for UBC Athletics and Recreation to implement into their current Intramurals program: improve the awareness and access to information regarding UBC Intramurals leagues, create a female hat league to allow small group sign-up, implement consistent schedules for intramurals leagues and games, increase the types of sports and tiers offered within the program, and to use a mixed-methods study on a larger sample size in future research for this topic.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".