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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".