Navigating Spaces of Belonging: Undergraduate Nursing Students’ Experiences in Online Learning Environments
Bibliographic record
Abstract
AIM: The aim of the study was to explore undergraduate nursing students' experiences of belonging while studying online during the COVID-19 pandemic. BACKGROUND: The use of online learning platforms increased drastically during the pandemic. Limited research exists on nursing students' experiences of belonging while studying online. METHOD: An explanatory sequential mixed-methods design was used for this study. This article reports on phase two, the qualitative component, which employed interpretive description methodology to understand nursing students' experiences. RESULTS: Ten semistructured interviews were completed, and four themes were identified: factors contributing to students' sense of belonging online, navigating the online learning environment, creating safe online spaces during times of uncertainty, and students' vision of developing a sense of belonging online. CONCLUSION: Fostering nursing students' sense of belonging is critical to their retention, persistence, and overall academic success when learning online.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".