A mixed study systematic review of social media in nursing and midwifery education: Protocol
Bibliographic record
Abstract
AIM: To synthesize evidence on the use of social media in nursing and midwifery education. BACKGROUND: Social media is one type of online platform that is being explored to determine if there is value in using interactive, digital communication tools to support how nurses and midwives learn in a variety of settings. DESIGN: A sequential explanatory synthesis approach will be used for this mixed study review. METHOD: Five bibliographic databases; PubMed, MEDLINE, CINAHL, Scopus, and ERIC will be searched using a combination of keywords relevant to social networking and social media, nursing and midwifery, and education. The search will not be limited by year of publication. Titles, abstracts, and full papers will be screened by two independent reviewers against inclusion and exclusion criteria, with any disagreements resolved via a third reviewer. Selected studies will undergo quality assessment and data extraction. Data synthesis will occur in three sequential phases, with quantitative and qualitative data analysed separately and then integrated where possible to provide a conceptual framework illustrating learning via social media. Funding for this review was confirmed in May 2016 by Sigma Theta Tau International and the National League for Nursing. DISCUSSION: The mixed study systematic review will produce the first rigorous synthesis on the use of social media in nursing and midwifery education and will have important implications for educators as well as students. It will also highlight knowledge gaps and make recommendations on the use of this novel technology in higher and continuing education.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| grok | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| opus | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.140 | 0.150 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.061 | 0.011 |
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, unvalidatedLabeled directly by 3 models reading the full record.
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".