Implementing a Facilitated Black Admissions Process in a Canadian Physiotherapy Programme
Bibliographic record
Abstract
Purpose: To report on the development, implementation, and evaluation of a facilitated Black admissions programme (FBAP) in a Canadian physiotherapy programme in 2023. Method: We observed and reported on the development and application process of a FBAP in a Master of Science Physiotherapy [MSc(PT)] programme, and the characteristics and results of applicants who applied through the FBAP and compared their interview experience with the conventional stream of applicants in the MSc(PT) programme. We summarized the candidate data from the streams using descriptive statistics and then compared the stream's interview score means and the evaluator scores between and within streams by calculating intraclass correlation coefficients and standard errors of measurements. Results: Our analysis revealed the mean scores of the FBAP were significantly higher than those of the conventional stream ( p = 0.018). There was no difference in the correlation between sub-GPA and interview scores for the two streams. In addition, the comparison of the interrater reliability of the FBAP (ICC: 0.76; 95% CI: 0.43, 0.90) and the conventional stream (ICC: 0.82; 95% CI: 0.79, 0.84), demonstrated alignment with no significant difference. Conclusions: In 2023, McMaster University successfully implemented the first iteration of the FBAP stream and fulfilled a vision that began in 2021 to increase the diversity of underrepresented minorities and decrease historical barriers for Black applicants in their MSc(PT) admissions processes.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".