To Blog or Not to Blog: What Do Nursing Faculty Think?
Bibliographic record
Abstract
BACKGROUND: Nurse educators find themselves tasked with developing content that both is aesthetically appealing and engages today's technological learners while empowering them to apply their knowledge in clinical and classroom settings. Students engaging with social networking systems reported increased satisfaction with collaborative peer-to-peer learning experiences, socialization, self-reflection, peer critique, problem-solving skills, collation of evidence-based resources, and instructor performance. METHOD: This project included identifying the needs of nursing faculty regarding the use of blogging in their courses and barriers faced by faculty with implementing blogging in nursing curricula. A convenience sampling method was used, with surveys e-mailed to 49 schools of nursing in Illinois and 38 in Ontario. RESULTS: One hundred twenty-two surveys were completed: 78 in Illinois and 44 in Ontario. Results suggest there are many pedagogical, philosophical, and ethical issues associated with using blogging and technology in nursing education. CONCLUSION: Although significant challenges exist, blogging and technology can be useful collaborative learning tools. [J Nurs Educ. 2016;55(12):683-689.].
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".