Comparison of admission rates to a non-traditional Doctor of Pharmacy programme using internet-based multiple-mini interviews versus on-site
Bibliographic record
Abstract
Objective: To evaluate if applicants selecting internet-based interviews (iMMI) have the same probability for admission as traditional on-site interviews (MMI). Methods: This was a retrospective cohort study of all applicants between 2014 and 2017 using routinely collected data. Logistic regression was used to compare the odds of admission and models were adjusted for potential confounders. Confounders which were significant in the univariate analysis were selected for the multivariate model. Analysis was performed using XLSTAT. Results: There were 238 applicants. Admission rate was not different between iMMI and MMI applicants. Eight-four percent (84%) of applicants in iMMI and 80% in MMI were admitted leading to a crude odds ratio (OR) of 1.4 (95% CI 0.7 - 2.7) and an adjusted OR of 1.2 (95% CI 0.5 - 2.8). Conclusions: This study demonstrated similar admission rates suggesting that iMMI may be a viable option for wider use. This process may improve admission procedures for hard-to-reach applicants in pharmacy programmes.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".