On a Knee-to-Know Basis: How Racialized Immigrants Navigate ACL Rehabilitation and Sports Medicine
Bibliographic record
Abstract
This thesis explores the experiences of first- and second-generation racialized immigrant women with sports medicine rehabilitation in Canada and the United States. Based on interviews with nine participants who have sustained a torn anterior cruciate ligament (ACL), this work reveals how factors such as social and cultural capital, trust, and health literacy shape how people navigate and manage athletic injury. Although ACL injury has been widely researched, the focus of extant literature is on biomedical advancements and psychological issues pertaining to the injury. There is minimal research on patient experiences through a critical, qualitative lens and even less about the specific experiences of racialized immigrants. By making these experiences visible and identifying barriers to care and recovery, this study can inform efforts to improve this population's access to quality sports medicine. It may also contribute to more broadly inclusive initiatives within healthcare systems.
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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 | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".