From concern to commitment
Bibliographic record
Abstract
Black students are systematically excluded from healthcare education, denying an increasing Black Canadian population access to diverse healthcare practitioners. This study explored admissions processes related to the inclusion of equity groups within McGill’s Occupational Therapy program and identified recommendations specific to Black applicants. Data was collected from a survey of current students and program graduates. Data analysis, applying a critical intersectional lens, was contextualized in dialogue with five key individuals at McGill and a review of the literature. Findings revealed an application process that foregrounds the experiences of white upper-class applicants while leaving historically excluded students feeling dissatisfied and unable to authentically represent themselves. Barriers to admissions in occupational therapy include a website with no Black representation, a difficult-to-navigate admissions guide, and an admissions committee currently lacking Black or Indigenous representation. The valorization of Black applicants and students through visibly welcoming spaces and meaningful resources for inclusion and success are acknowledged, but not yet developed. The study findings support next steps by providing program-level recommendations such as the inclusion of Black faculty on the admissions committee and the option to submit an essay for Black applicants. The implementation of an evidence-based Black Student Pathway is necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.013 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".