The Social Media Editor at Medical Journals: Responsibilities, Goals, Barriers, and Facilitators
Bibliographic record
Abstract
PURPOSE: To determine the responsibilities of journal social media editors (SMEs) and describe their goals and barriers and facilitators to their position. METHOD: The authors identified SMEs using an informal listserv and snowball sampling. Participants were interviewed (June-July 2016) about their position, including responsibilities; goals; barriers and facilitators; and attitudes and perceptions about the position. Themes were identified through a thematic analysis and consensus-building approach. Descriptive data, including audience metrics and 2016 impact factors, were collected. RESULTS: Thirty SMEs were invited; 24 were interviewed (19 by phone and 5 via e-mail). SMEs generally had a track record in the social media community before being invited to be SME; many had preexisting roles at their journal. Responsibilities varied considerably; some SMEs also served as decision editors. Many SMEs personally managed journal accounts, and many had support from nonphysician journal staff. Consistently, SMEs focused on improving reader engagement by disseminating new journal publications on social media. The authors identified goals, resources, and sustainability as primary themes of SMEs' perspectives on their positions. Editorial leadership support was identified as a key facilitator in their position at the journal. Challenges to sustainability included a lack of tangible resources and uncertainty surrounding, or a lack of, academic credit for social media activities. CONCLUSIONS: Many of the participating SMEs pioneered the use of social media as a platform for knowledge dissemination at their journals. While editorial boards were qualitatively supportive, SMEs were challenged by limited resources and lack of academic credit for social media work.
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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 | 0.036 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".