Criteria for social media-based scholarship in health professions education
Bibliographic record
Abstract
BACKGROUND: Social media are increasingly used in health professions education. How can innovations and research that incorporate social media applications be adjudicated as scholarship? OBJECTIVE: To define the criteria for social media-based scholarship in health professions education. METHOD: In 2014 the International Conference on Residency Education hosted a consensus conference of health professions educators with expertise in social media. An expert working group drafted consensus statements based on a literature review. Draft consensus statements were posted on an open interactive online platform 2 weeks prior to the conference. In-person and virtual (via Twitter) participants modified, added or deleted draft consensus statements in an iterative fashion during a facilitated 2 h session. Final consensus statements were unanimously endorsed. RESULTS: A review of the literature demonstrated no existing criteria for social media-based scholarship. The consensus of 52 health professions educators from 20 organisations in four countries defined four key features of social media-based scholarship. It must (1) be original; (2) advance the field of health professions education by building on theory, research or best practice; (3) be archived and disseminated; and (4) provide the health professions education community with the ability to comment on and provide feedback in a transparent fashion that informs wider discussion. CONCLUSIONS: Not all social media activities meet the standard of education scholarship. This paper clarifies the criteria, championing social media-based scholarship as a legitimate academic activity in health professions education.
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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 | 0.022 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".