Pharmacy students can improve access to quality medicines information by editing Wikipedia articles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Pharmacy training programs commonly ask students to develop or edit drug monographs that summarize key information about new medicines as an academic exercise. We sought to expand on this traditional approach by having students improve actual medicines information pages posted on Wikipedia. METHODS: We placed students (n = 119) in a required core pharmacy course into groups of four and assigned each group a specific medicines page on Wikipedia to edit. Assigned pages had high hit rates, suggesting that the topics were of interest to the wider public, but were of low quality, suggesting that the topics would benefit from improvement efforts. We provided course trainings about editing Wikipedia. We evaluated the assignment by surveying student knowledge and attitudes and reviewing the edits on Wikipedia. RESULTS: Completing the course trainings increased student knowledge of Wikipedia editing practices. At the end of the assignment, students had a more nuanced understanding of Wikipedia as a resource. Student edits improved substantially the quality of the articles edited, their edits were retained for at least 30 days after course completion, and the average number page views of their edited articles increased. CONCLUSIONS: Our results suggest that engaging pharmacy students in a Wikipedia editing assignment is a feasible alternative to writing drug monographs as a classroom assignment. Both tasks provide opportunities for students to demonstrate their skills at researching and explaining drug information but only one serves to improve wider access to quality medicines information. Wikipedia editing assignments are feasible for large groups of pharmacy students and effective in improving publicly available information on one of the most heavily accessed websites globally.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it