What Drives Nursing Students’ Social Media-Related Decisions in their Learning Practices? A Survey of an Ontario School of Nursing
Bibliographic record
Abstract
Social media can provide a tool for nursing students to consolidate their formal and informal learning experiences. The objective of this study was to explore what drives nursing students’ decisions to use social media in their formal and informal learning. This study surveyed 220 nursing students at one Ontario School of Nursing. IBM SPSS (v. 24) was used to calculate frequency counts, Chi-Square Tests for Independence, and Spearman Correlations. A modified directed content analysis was conducted on the open-ended response data. Numerous drivers affect nursing students’ decisions to use social media for learning purposes. These include: 1) their nursing programs; 2) their professors’ attitudes towards social media; 3) their age and perceived levels of experience using social media; 4) the convenience of accessing learning content online; and 5) barriers like privacy, quality and reliability of social media-based learning content, and the potential for distraction. As a result of these findings, there is a strong rationale to further explore the ways in which social media can be used in both formal and informal nursing education.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".