Developing a Customized Multiple Interview for Dental School Admissions
Bibliographic record
Abstract
From the early 1980s until recently, the University of British Columbia Faculty of Dentistry had employed the Canadian Dental Association (CDA) Structured Interview in its Phase 2 admissions process (with those applicants invited for interviews). While this structured interview had demonstrated reliability and validity, the Faculty of Dentistry came to believe that a multiple interview process using scenarios would help it better identify applicants who would match its mission. After a literature review that investigated such interview protocols as unstructured, semi-structured, computerized, and telephone formats, a multiple interview format was chosen. This format was seen as an emerging trend, with evidence that it has been deemed fairer by applicants, more reliable by interviewers, more difficult for applicants to provide set answers for the scenarios, and not to require as many interviewers as other formats. This article describes the process undertaken to implement a customized multiple interview format for admissions and reports these outcomes of the process: a smoothly running multiple interview; effective training protocols for staff, interviewers, and applicants; and reports from successful applicants and interviewers that they felt the multiple interview was a more reliable and fairer recruiting tool than other models.
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 | 0.082 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".