Why Medical Schools Should Embrace Wikipedia: Final-Year Medical Student Contributions to Wikipedia Articles for Academic Credit at One School
Bibliographic record
Abstract
PROBLEM: Most medical students use Wikipedia as an information source, yet medical schools do not train students to improve Wikipedia or use it critically. APPROACH: Between November 2013 and November 2015, the authors offered fourth-year medical students a credit-bearing course to edit Wikipedia. The course was designed, delivered, and evaluated by faculty, medical librarians, and personnel from WikiProject Medicine, Wikipedia Education Foundation, and Translators Without Borders. The authors assessed the effect of the students' edits on Wikipedia's content, the effect of the course on student participants, and readership of students' chosen articles. OUTCOMES: Forty-three enrolled students made 1,528 edits (average 36/student), contributing 493,994 content bytes (average 11,488/student). They added higher-quality and removed lower-quality sources for a net addition of 274 references (average 6/student). As of July 2016, none of the contributions of the first 28 students (2013, 2014) have been reversed or vandalized. Students discovered a tension between comprehensiveness and readability/translatability, yet readability of most articles increased. Students felt they improved their articles, enjoyed giving back "specifically to Wikipedia," and broadened their sense of physician responsibilities in the socially networked information era. During only the "active editing months," Wikipedia traffic statistics indicate that the 43 articles were collectively viewed 1,116,065 times. Subsequent to students' efforts, these articles have been viewed nearly 22 million times. NEXT STEPS: If other schools replicate and improve on this initiative, future multi-institution studies could more accurately measure the effect of medical students on Wikipedia, and vice versa.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".