Effectiveness of blended learning in pharmacy education: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Though blended learning (BL), is widely adopted in higher education, evaluating effectiveness of BL is difficult because the components of BL can be extremely heterogeneous. Purpose of this study was to evaluate the effectiveness of BL in improving knowledge and skill in pharmacy education. METHODS: PubMed/MEDLINE, Scopus and the Cochrane Library were searched to identify published literature. The retrieved studies from databases were screened for its title and abstracts followed by the full-text in accordance with the pre-defined inclusion and exclusion criteria. Methodological quality was appraised by modified Ottawa scale. Random effect model used for statistical modelling. KEY FINDINGS: A total of 26 studies were included for systematic review. Out of which 20 studies with 4525 participants for meta-analysis which employed traditional teaching in control group. Results showed a statistically significant positive effect size on knowledge (standardized mean difference [SMD]: 1.35, 95% confidence interval [CI]: 0.91 to 1.78, p<0.00001) and skill (SMD: 0.68; 95% CI: 0.19 to 1.16; p = 0.006) using a random effect model. Subgroup analysis of cohort studies showed, studies from developed countries had a larger effect size (SMD: 1.54, 95% CI: 1.01 to 2.06), than studies from developing countries(SMD: 0.44, 95% CI: 0.23 to 0.65, studies with MCQ pattern as outcome assessment had larger effect size (SMD: 2.81, 95% CI: 1.76 to 3.85) than non-MCQs (SMD 0.53, 95% CI 0.33 to 0.74), and BL with case studies (SMD 2.72, 95% CI 1.86-3.59) showed better effect size than non-case-based studies (SMD: 0.22, CI: 0.02 to 0.41). CONCLUSION: BL is associated with better academic performance and achievement than didactic teaching in pharmacy education.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".