A Qualitative Exploration of the Teaching- and Learning-Related Content Nursing Students Share to Social Media
Bibliographic record
Abstract
Background: Social media have many applications in health professions education. The current literature focuses on how faculty members use social media to supplement their teaching; less is known about how the students themselves use social media to support their educational activities. In this study, this digital artifact collection qualitatively explored what educational content nursing students shared with their social media accounts. Methods: A total of 24 nursing students’ Facebook, Twitter, and Instagram accounts were followed over 5 months. A modified directed content analysis was conducted weekly and at the end of the data collection period, using two cycles of inductive and deductive coding. Results: This study demonstrated that nursing students used social media to combat isolation, to consolidate course content, to share resources, and to better anticipate the transition to practice as a new nurse. Conclusions: Faculty members can capitalize on social media platforms to help nursing students explore nursing roles and identities while learning about and enacting professional online behaviours.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".