Developing a Web-Based Asynchronous Case Discussion Format on Social Media to Teach Clinical Reasoning: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Case-based learning conferences are valuable to trainees, but growing clinical demands hinder consistent attendance. Social media increasingly acts as a venue for trainees to supplement their education asynchronously. We designed and implemented a web-based asynchronous clinical case discussion series on the Twitter social media platform to fill this educational gap. OBJECTIVE: The aim of this mixed methods study is to examine the nature of interactions among web-based case discussion participants and assess local attitudes regarding the educational intervention. METHODS: Starting in February 2018, we posted clinical vignettes to a dedicated Twitter account with the prompt "What else do you want to know?" to stimulate discussion. The authors replied in real time when case discussion participants requested additional details. Additional data about the case were posted at regular intervals to the discussion thread to advance the overall case discussion. Participants were asked to explain their reasoning and support their conclusions when appropriate. Web-based engagement was assessed using Twitter Analytics. Participants' posts were qualitatively analyzed for themes, with special attention to examples of using clinical reasoning skills. A codebook of types of participant posts and interactions was refined iteratively. Local engagement and attitudes at our institution were assessed by surveying internal medicine trainees (n=182) and faculty (n=165) after 6 months. RESULTS: Over a 6-month period, 11 live case discussions were engaged with by users 1773 times. A total of 86 Twitter profiles spanning 22 US states and 6 countries contributed to discussions among participants and the authors. Participants from all training levels were present, ranging from students to faculty. Interactions among participants and the case moderators were most commonly driven by clinical reasoning, including hypothesis-driven information gathering, discussing the differential diagnosis, and data interpretation or organization. Of 71 respondents to the local survey, 29 (41%) reported having a Twitter account. Of the 29 respondents with Twitter accounts, 17 (59%) reported participating in the case discussions. Respondents agreed that case participation increased both their clinical reasoning skills (15/17, 88%) and clinical knowledge (13/17, 76%). CONCLUSIONS: A social media-based serialized case discussion was a feasible asynchronous teaching method for engaging web-based learners of all levels in a clinical reasoning discussion. Further study should examine what factors drive trainee participation in web-based case discussions and under what circumstances asynchronous discussion might be preferred over in-person teaching activities.
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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 | 0.057 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".