Documenting Social Media Engagement as Scholarship: A New Model for Assessing Academic Accomplishment for the Health Professions
Bibliographic record
Abstract
BACKGROUND: The traditional model of promotion and tenure in the health professions relies heavily on formal scholarship through teaching, research, and service. Institutions consider how much weight to give activities in each of these areas and determine a threshold for advancement. With the emergence of social media, scholars can engage wider audiences in creative ways and have a broader impact. Conventional metrics like the h-index do not account for social media impact. Social media engagement is poorly represented in most curricula vitae (CV) and therefore is undervalued in promotion and tenure reviews. OBJECTIVE: The objective was to develop crowdsourced guidelines for documenting social media scholarship. These guidelines aimed to provide a structure for documenting a scholar's general impact on social media, as well as methods of documenting individual social media contributions exemplifying innovation, education, mentorship, advocacy, and dissemination. METHODS: To create unifying guidelines, we created a crowdsourced process that capitalized on the strengths of social media and generated a case example of successful use of the medium for academic collaboration. The primary author created a draft of the guidelines and then sought input from users on Twitter via a publicly accessible Google Document. There was no limitation on who could provide input and the work was done in a democratic, collaborative fashion. Contributors edited the draft over a period of 1 week (September 12-18, 2020). The primary and secondary authors then revised the draft to make it more concise. The guidelines and manuscript were then distributed to the contributors for edits and adopted by the group. All contributors were given the opportunity to serve as coauthors on the publication and were told upfront that authorship would depend on whether they were able to document the ways in which they met the 4 International Committee of Medical Journal Editors authorship criteria. RESULTS: We developed 2 sets of guidelines: Guidelines for Listing All Social Media Scholarship Under Public Scholarship (in Research/Scholarship Section of CV) and Guidelines for Listing Social Media Scholarship Under Research, Teaching, and Service Sections of CV. Institutions can choose which set fits their existing CV format. CONCLUSIONS: With more uniformity, scholars can better represent the full scope and impact of their work. These guidelines are not intended to dictate how individual institutions should weigh social media contributions within promotion and tenure cases. Instead, by providing an initial set of guidelines, we hope to provide scholars and their institutions with a common format and language to document social media scholarship.
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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 | 0.042 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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; both teacher heads agree on what is shown here.
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".