Accreditation of pharmacy programs and its impact on SPLE success and pharmacist readiness in Saudi Arabia
Bibliographic record
Abstract
Aim The impact of pharmacy program accreditation on the Saudi Pharmacists Licensure Examination (SPLE) pass rates and overall pharmacist readiness was investigated. Methods A cross-sectional retrospective study was conducted. Data on SPLE pass rates were obtained from the Saudi Commission for Health Specialties (SCFHS) 2024 report. Pharmacy colleges were categorized into five groups based on their students' average SPLE scores. Information on the national i.e., the Evaluation and Training Evaluation Center (ETEC) and international i.e., the American Council for Pharmacy Education (ACPE) and the Canadian Council for Accreditation of Pharmacy Programs (CCAPP) accreditation status of these colleges was also collected. Results Higher average SPLE scores (mean = 563, SE = 43.4) were observed in accredited colleges (either national or international) compared to non-accredited colleges (mean = 533, SE = 33.6), with a significant difference noted [t(22) = −2.149, p = 0.042]. Higher average SPLE scores (mean = 581.8, SE = 18.9) were also found in colleges with multiple accreditations compared to those with fewer or no accreditations (mean = 548.02, SE = 18.9), though this difference was not statistically significant [t(25) = −1.8, p = 0.086]. Discussion and conclusion It was demonstrated that accreditation, whether national or international, is associated with higher SPLE pass rates, indicating a positive impact on exam performance. National accreditation by ETEC alone was found to be sufficient for improving SPLE scores and ensuring pharmacist readiness, whereas dual or international accreditations did not provide additional benefits in this context.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".