Exploring differences in perceptions around Social Media Competencies: An Expert vs. Frontline User Study
Bibliographic record
Abstract
Aim: Scholarly communities increasingly interact using social media (SoMe). This study investigated curricular expectations of expert and frontline SoMe users, with the goal of identifying differences that might inform the development of a curriculum designed to teach clinicians and researchers the effective use of SoMe.Methods: From May 15 to August 28, 2020, we recruited participants via the METRIQ study recruitment protocol. Participants were stratified into “expert” and “frontline” users based on prior experience with SoMe. “Expert” users were defined as having published SoMe research, run SoMe workshops, or through the use of a popular #SoMe account. All others were categorized as “frontline” users. Participants completed a 14-question survey (with 90 sub-questions) regarding the content, skills, and attitudes that they believed should be taught to educators or researchers new to SoMe.Results: In total, 224 users were invited, and 184 users filled out the survey. Experts were more likely to recommend teaching clinicians to use blogs (88% vs 74%), Facebook (46% vs 32%), Instagram (51% vs 34%), Medium (16% vs 4%), Snapchat (15% vs 4%), TikTok (29% vs 12%), and Twitter (97% vs 88%) compared to frontline users. Experts were more likely to recommend SoMe to foster communities of practice (83% vs 66%), disseminate research (80% vs 67%), and promote engagement for knowledge translation (86% vs 74%) compared to frontline users. Conclusions: There are few differences between the SoMe curricular expectations of expert vs. frontline users. These results could inform the creation of resources for teaching clinicians and researchers how to effectively use SoMe.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".