Systems to support scholarly social media: a qualitative exploration of enablers and barriers to new scholarship in academic medicine
Bibliographic record
Abstract
INTRODUCTION: As academia begins to incorporate modern communication technologies into its scholarly structures, there are both enablers and barriers which foster academics' uptake of these innovations. Those who are early adopters of academic social media - whether it be for education, research-related networking, or knowledge translation - may therefore be best positioned to highlight both enablers and barriers within their work environments. METHODS: The authors conducted a constructivist grounded theory study to discern what prominent practitioners of academic social media (e.g. Twitter) have encountered in their careers. Participants were recruited via a snowball sampling technique and invited to participate in semi-structured interviews. Three investigators engaged in constant comparative analysis of incoming transcripts. To enhance rigour, we conducted an audit of the analysis and a participant member check. RESULTS: Seventeen emerging influencers in the field of academic social media were recruited. After axial coding, the 30 enablers and 21 barriers to academic social media use were mapped to three spheres of influence: personal, institutional, and virtual. The investigators propose a framework that organizes these enablers and barriers around a tipping point where sustainability becomes possible. CONCLUSIONS: Multiple enablers and barriers were described to influence social media users within academic medicine. By organizing these facets into a personal, institutional, and virtual framework along a spectrum, we can begin to understand the underlying structures that potentiate the academic ecosystems in which social media and similar innovations may flourish.
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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 | 0.008 | 0.427 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".