Evaluating the Effectiveness of the Computer-Based Education Platform, Pharmacy5in5, on Pharmacists’ Knowledge of Anticholinergic Toxicity Using a Randomized Controlled Trial
Bibliographic record
Abstract
Background: Computer-based education has been widely implemented in healthcare professional development education. However, there has been little examination of the potential for computer-based education to enhance pharmacists’ knowledge. This study aims to assess the effectiveness of computer-based education on improving pharmacists’ knowledge compared to printed education material. Methods: This study was a web-based randomized controlled trial. Participants were randomly allocated to either an intervention group where they had access to the computer-based education module on Pharmacy5in5.ca or to a control group where they had access to printed educational material. Knowledge gain was assessed using a pre- and post-knowledge test. Results: A total of 120 pharmacists were recruited and 101 completed the post-knowledge test (50/60 in the intervention group; 51/60 in the control group). Both groups showed a significant increase in knowledge gain (intervention group: pre-test mean score 19.35 ± 3.56, post-test mean score 22.42 ± 3.812, p value < 0.001; control group pre-test mean score 19.22 ± 3.45, post-test mean score 23.29 ± 3.087, p value < 0.001). However, the difference in knowledge change was not significant between the two groups (22.42 vs. 23.29, p value = 0.333). Conclusions: In this study, a computer-based education module enhanced pharmacists’ knowledge to a similar degree to printed education material. Efforts should be made to provide computer-based education as an option to support pharmacists’ professional development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".