Identifying Holistic Admissions Practices in Health Sciences Programs in Canada for Equity-Deserving Groups: A Scoping Review Informing Admissions Guideline Development at Queen’s Health Sciences
Bibliographic record
Abstract
Many Canadian health sciences programs have committed to increasing inclusive admissions. Yet, equity-deserving groups – including individuals who identify as Indigenous, racialized, female, 2SLGBTQI+, and those from low socioeconomic or rural backgrounds remain underrepresented. In alignment with the Queen’s Health Sciences (QHS) Equity, Diversity, Inclusion, Indigeneity, Accessibility (EDIIA) Action Plan, this scoping review aims to identify best practices for enhancing holistic admissions of individuals from equity-deserving backgrounds to better reflect and serve patient populations in Canada. The primary research questions explored are: I) what evidence-based and equity-informed admissions practices support the enrolment of equity-deserving groups in health sciences undergraduate and graduate programs in Canada; II) how are transparent and holistic admissions practices implemented in health sciences programs to support equity-deserving groups; and III) how can these admissions practices inform the development of guidelines to enhance the admissions of equity-deserving groups across QHS programs. We included literature surrounding EDIIA-based admissions processes in health sciences programs in Canadian universities. Suitable literature includes proposed interventions, case studies with tested interventions, reviews of existing practices, and data that has been collected through interviews, surveys, and student feedback. Relevant references were extracted from ERIC, CINAHL, MEDLINE, and EMBASE and are being screened using Covidence. A grey literature search was completed by the faculty supervisor using Google’s Advanced Search. Data extraction by at least two reviewers will commence and be followed by manuscript drafting. In alignment with the QHS EDIIA Action Plan, the results of this study can inform best practices for implementing holistic admissions processes at QHS, ultimately fostering diverse healthcare workforces. Through conducting a scoping review with a fourteen person working group, we hope to capture a broad range of perspectives and lived experiences, embodying qualities of diversity and inclusion that we aim to embody throughout the research process.
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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 | 0.033 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.031 | 0.051 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".