Social media in nursing and midwifery education: A mixed study systematic review
Bibliographic record
Abstract
AIM: To synthesize evidence on the effectiveness of social media in nursing and midwifery education. BACKGROUND: Social media are being explored to see if these online tools can support teaching, learning, and assessment. DESIGN: A mixed study systematic review. DATA SOURCES: A systematic search of PubMed, MEDLINE, CINAHL, Scopus, and ERIC was run in January 2016. An updated search was run in June 2017. No date limits were applied. METHODS: Titles, abstracts, and full papers were screened against inclusion criteria by two independent reviewers, who extracted and quality assessed data. Synthesis followed a sequential explanatory approach. RESULTS: Twelve studies were included. Social media seemed to support students to acquire new knowledge and skills. The learning process centred on the interactive nature of the platforms which allow information to be dynamically shared and discussed in near real time. The characteristics of social media enabled social support and a more student-centred setting, which appeared to enhance collaborative learning, although information quality was sometimes problematic. Learning via social media was underpinned by how well the educational interventions were organized, digital literacy and e-Professionalism of students and faculty, the accessibility of the online applications, and personal motivation. CONCLUSION: This review provides the first rigorous synthesis of social media in nursing and midwifery education. A new Social Media Learning Model was conceptualized to aid our understanding of learning via this technology. Knowledge gaps are identified and recommendations on how to capitalize on social media to improve learning in higher and continuing education provided.
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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 | 0.039 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".