Association Between Prerequisites and Academic Success at a Canadian University’s Pharmacy Program
Bibliographic record
Abstract
Objective. To identify pharmacy prerequisites associated with academic success in the current Bachelor of Science in Pharmacy (BSP) program and anticipated success in the planned Doctor of Pharmacy (PharmD) program at the University of Saskatchewan. Methods. Statistical analysis was conducted on retrospective data of the grades of 1,236 pharmacy students admitted from 2002 to 2015. BSP success was calculated using a weighted average of all required courses within the BSP program. Anticipated success in the PharmD program was calculated from the BSP grades after excluding PharmD prerequisites currently part of the BSP. Models of BSP and PharmD prerequisites and demographic variables associated with pharmacy program success were constructed using stepwise and forced linear regression. Results. For the current BSP program, modelling explained more than half of academic success in year 1. Explicable variance declined each year, explaining less than 20% in year 4. After removing PharmD prerequisites from the program, the BSP prerequisites associated with success were the same as the first model but explained less of the variance in years 1 and 2. Using both BSP and the new PharmD prerequisites explained nearly three-quarters of the variance in year 1 for the remaining pharmacy courses. Explicable variance increased slightly in year 2, declined to approximately two-thirds in year 3 and just over one-half in year 4. Conclusion. Consistency of instructor and course content, along with instructional design and higher-level learning, may explain these stronger associations for the PharmD prerequisites.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".