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 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 | 0.260 | 0.438 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.016 | 0.052 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".