A Critical Analysis of Online Nursing Education: Balancing Optimistic and Cautionary Perspectives
Bibliographic record
Abstract
The landscape of nursing education has been transformed by increasing student demand for online programs coupled with strong institutional directives to deliver nursing courses through distributed learning. The authors present a qualitative research design informed by philosophical hermeneutics in which 30 undergraduate and graduate nursing students discuss their experiences of the influence of peer dynamics on online learning. The findings include issues related to time, demands of online participation, experiences of conflict, and the development of skills in the online environment. Theoretical matters of curriculum such as instrumentality and tensionality are examined, generating both optimistic and cautionary possibilities for online learning. Online nursing students could benefit from a period of face-to-face orientation with a focus on building intellectual and social communities, limited class size, and opportunities to connect learners.
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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 | 0.107 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.022 | 0.059 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".