Teaching Evidence-Based Medicine to Medical Students Using Wikipedia as a Platform
Bibliographic record
Abstract
PROBLEM: While ideal curricular structures for effective teaching of evidence-based medicine (EBM) have not been definitively determined, optimal strategies ensure that EBM teaching is interactive and clinically based, aligns with major trends in education and health care, and uses longitudinally integrated, whole-task activities. APPROACH: The authors developed a longitudinal, semester-long project, embedded in a first-year medicine course, through which they taught EBM using Wikipedia as a platform. Students worked individually and in small groups to choose a medicine-related Wikipedia article, identify information gaps, search for high-quality resources, appraise the sources, and incorporate the new information into the article (i.e., by editing Wikipedia). Students also applied their new appraisal skills to critique a second article. The authors used an online tool to track and record student editing, and they obtained qualitative data on student perceptions of the project via survey. Duplicate marking of a sample of assignments was performed using the Valid Assessment of Learning in Undergraduate Education critical thinking rubric developed by Finley and Rhodes. OUTCOMES: In fall 2017, 101 students made over 1,000 unique edits to 16 online Wikipedia articles, adding over 10,000 words. Through thematic analysis of qualitative data, the authors highlighted several aspects of the project that students appreciated, as well as barriers related to completing their projects. Correlation of the 17 consenting students' final assignments with the critical thinking rubric supports the assignment structure as a tool for assessing critical thinking. NEXT STEPS: This authentic task adheres to the principles of high-quality EBM instruction and could be implemented by a variety of health care educational programs. Modifications to the delivery model are underway to address challenges identified.
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 | 0.023 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".