Use of Social Media and Free Open Access Medicine (FOAM) for Continuing Education in Emergency Nursing: A Scoping Review
Bibliographic record
Abstract
Objective: Emergency nurses are responsible for ensuring that they have up-to-date knowledge and skills to deal with any situation that may present in clinical practice. As an emerging trend for learners to obtain and discuss evidence-based medical education, social media, Free Open Access Medical education (FOAM) and Free Open Access Nursing education (FOAN) could be used for continuing education in emergency nursing. This scoping review aims to discover what is known about social media and FOAM in continuous emergency nursing education. Methods: This scoping review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses – Extension for Scoping Reviews (PRISMA-ScR). The review searched three databases (Medline, CINAHL, and Web of Science) for any paper that discussed social media or FOAM in the context of continuing education in either general or emergency nursing published in any year. Of the 346 records screened, 12 studies were reviewed for their description of social media for continuing education use and any reported challenges or outcomes. Results: The current state of literature is limited in describing the use of social media with continuing education specifically in emergency nursing. Rather than as a primary educational intervention, social media has been used to enhance other educational strategies or as in-the-moment forms of learning. Studies that utilized social media showed favorability to its use, but there were often challenges to the methodology of these studies. Despite papers describing the elements of FOAM, papers within the nursing literature did not use this term. Conclusion: Although the literature on social media in emergency and general nursing literature is growing, it is superficial and broad. More studies are needed to see the overall effects of social media in continuing education in emergency nursing. Keywords: continuing education, emergency nursing, nursing education, social media.
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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 | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".