Enculturating a Community of Action (CoA): A qualitative study of health professions educators’ perspectives on teaching with Wikipedia
Bibliographic record
Abstract
Abstract Purpose There is a growing desire for health professions educators to engage learners in more meaningful instruction. Many have tapped Wikipedia to offer an applied approach to engage learners, particularly as it relates to evidence-based medicine (EBM). However, little is known about the benefits and challenges of using Wikipedia as a pedagogical tool from the collective experience of educators who have sought to improve their instructional practice with it. This study aims to uncover and synthesize the perspectives of health professions education (HPE) instructors who have incorporated Wikipedia in their HPE courses. Methods Applying a constructivist approach, the authors conducted semi-structured interviews with 17 participating HPE instructors who had substantively integrated Wikipedia into their curriculum. Participants were interviewed about their experiences of integrating Wikipedia editing into their courses. Thematic analysis was conducted on resulting transcripts. Results Authors observed two broad themes among participants’ expressed benefits of teaching with Wikipedia: 1) provides a meaningful instructional alternative that also benefits society and develops learners’ information literacy and EBM skills, and 2) supports learners’ careers and professional identity formation. Identified challenges included: 1) high effort and time, 2) issues with sourcing references, and 3) challenging interactions with skeptics, editors, and students. Discussion Findings build on known benefits, such as providing a real-world collaborative project that contextualizes students’ learning experiences. They also echo known challenges, such as the resource-intensive nature of teaching with Wikipedia. At the same time, findings extend the current literature by revealing a potential opportunity to approach crowd-sourced information tools, like Wikipedia, as a vehicle to engage and enculturate HPE students within a situated learning context. These findings present implications for HPE programs considering implementing Wikipedia and faculty development needed to help instructors harness crowd-sourced information tools’ pedagogical opportunities as well as anticipate their challenges.
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 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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".