Nursing students’ use of social media in their learning: a case study of a Canadian School of Nursing
Bibliographic record
Abstract
BACKGROUND: Social media has diverse applications for nursing education. Current literature focuses on how nursing faculty use social media in their courses and teaching; less is known about how and why nursing students use social media in support of their learning. OBJECTIVES: The purpose of this study was to explore how nursing students use social media in their learning formally and informally. METHODS: This exploratory qualitative case study of a Canadian School of Nursing reports on the findings of interviews (n = 9) with nursing students to explore how they use social media in their learning. Data were analyzed using a combined deductive and inductive coding approach, using three cycles of coding to facilitate category identification. RESULTS AND CONCLUSIONS: The findings demonstrate that participants use social media for formal and informal learning and specifically, as a third space to support their learning outside of formal institutional structures. Social media plays a role in the learning activities of nursing students studying both face-to-face and by distance. Accordingly, social media use has implications for learning theory and course design, particularly regarding creating space for student learning communities.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.046 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".