Adapting to change: Undergraduate nursing students' sense of belonging transitioning from online to in‐person learning environments
Bibliographic record
Abstract
AIM: To examine undergraduate nursing students' sense of belonging as they transitioned from online to in-person learning. DESIGN: A mixed-method design employing a Sense of Belonging Survey and three open-ended questions. METHODS: Participants were first-year undergraduate nursing students who were back to in-person learning after 3 years of online learning during the pandemic. The survey was administered online in April 2023 using Qualtrics survey software. The survey data were analysed using descriptive statistics, and the open-ended questions were analysed by deductive thematic analysis. RESULTS: Seventy-five (48%) of the 155 potential participants responded to the survey. The mean score on the Sense of Belonging Survey was 74%, a positive finding suggesting that many participants feel that they 'belong' in the classroom. Three overarching themes were identified in response to the open-ended questions: factors supporting students' sense of belonging, factors hindering students' sense of belonging and strategies for faculty, administrators and students to increase a sense of belonging. CONCLUSION: Understanding the factors that contribute to or hinder nursing students' sense of belonging during this transition will assist in developing strategies to mitigate challenges, foster a positive learning environment and enhance the overall sense of belonging among nursing students. IMPACT: The first year of a nursing programme is crucial for student retention as students require tailored programmes and strategies to support their success. Examining and analysing the transition from online to in-person classroom settings is crucial to identifying strategies to enhance and support first-year students' sense of belonging and academic success. Exploring nursing students' experiences of belonging during transitions contributes to a more inclusive and equitable educational experience, fostering an environment where all students can thrive and succeed. PATIENT OR PUBLIC CONTRIBUTION: Not applicable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".