Estimation of financial returns on investment in bridging education in pharmacy
Bibliographic record
Abstract
Background: Determining financial value of educational programs is an important vehicle for demonstrating accountability and responsibility to stakeholders. Several methods of estimating financial value and economic impact of educational programs have been proposed, including the throughput value model, the benefit cost ratio (BCR), and the return on investment (ROI) model. Objectives: To estimate the financial value of a bridging education program for internationally educated pharmacists seeking licensure in Canada. Methods: Three separate studies were undertaken utilizing the Throughput Value Model, the BCR and the ROI model. Results: All three models estimated positive benefits for students involved in bridging education in pharmacy. The Throughput Value Model estimated positive in each of years 1, 5 and 15, following completion of the program. The BCR model and the ROI models both estimated values of greater than 1, indicating positive financial returns from bridging education in pharmacy. Conclusions: While certain methodological limitations are inherent in estimating financial value of educational programs, all three studies were positive, highlighting the economic importance of bridging education. While financial value is one measure of success, other humanistic and social justice outcomes must also be considered when evaluating overall objectives of any educational program.
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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 | 0.017 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".