Social Media Use to Promote Nursing Scholarship
Bibliographic record
Abstract
Social media is a powerful tool to promote and communicate nursing scholarship. As the nursing profession evolves with advances in technology, platforms including X, LinkedIn, and TikTok offer unique opportunities for academic engagement and professional networking at the individual and collective level. This editorial explores the growing role of social media use in nursing scholarship, and highlights its potential to bridge the gap between clinical practice, research, and education, by introducing practical strategies for new and experienced users to leverage their existing work and reach new audiences including: themed content posting, showcasing initiatives, and creative research dissemination methods. Despite its benefits, effective use of social media in nursing scholarship also requires awareness of potential risks including concerns about maintaining professionalism, data privacy, and upholding ethics. Drawing from personal experiences, this editorial provides recommendations for developing a professional digital presence, avoiding public backlash, and fostering a respectful online community.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".