Exploring Fairness and Opportunity in the Admissions Process of Canadian Dental Schools
Bibliographic record
Abstract
Decades of evidence have demonstrated a lack of workforce diversity and sustaining disparities in dentistry. Underrepresented minority students may face challenges and implicit bias during the den-tal schools’ admission/selection process – which perpetuates the disparities. This dissertation want-ed to explore the fairness and opportunities for people who have been prevented from studying dentistry – more specifically, the link between Equity-Diversity-Inclusion (EDI) and the admission practices in dental schools. The literature has shown a growth in publications on equity measures during dental admission/selection processes, indicating a positive movement in this field. The ef-forts performed by dental schools to increase the representation of minorities in the profession have been made and become more internationalized and multifaceted recently. Some strategies to address both access and retention challenges included: prioritization of holistic approaches (during selection of candidates) and implementation of recruitment programs (which combine mentoring opportuni-ties and skills-based activities). Nevertheless, while these initiatives have demonstrated progress toward a more equitable dental workforce, sustained commitment and adaptation of strategies are essential to ensure lasting and meaningful change. The second and third parts of this dissertation focused on Canadian institutions’ admissions practices. Through a document analysis of dental schools’ websites (visual and written information), I noticed that the concept of EDI is connected to the admission process by showing prospective students that the university environment is welcom-ing, globalized, and that diverse students are embraced by the institution. But the websites’ commu-nication had several problems, including the poor choice of words and the lack of an equity defini-tion and equity information. At last, through semi-structured interviews, I interviewed the inter-viewers of the admission processes. Ten individuals who had acted as interviewers in dental schools’ admissions were vocal about the strengths and weaknesses in the process. Predominantly, participants criticized what they consider a defective process. The participants’ experiences revealed a tension between objectivity and holistic evaluation. This dissertation suggests adopting positive and alternative strategies, such as integrating equity-focused strategies for people involved in the admission process. These efforts require a combination of human, financial, and strategic resources.
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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 | 0.034 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.048 | 0.016 |
| Scholarly communication | 0.020 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| 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".