Exploring Belongingness in an Accelerated Nursing Program: A Qualitative Study
Bibliographic record
Abstract
Accelerated baccalaureate nursing programs have proliferated to meet the demands of the current and projected nursing shortage. While these programs serve an important function, accelerated nursing students face unique challenges related to fast pacing, content-heavy courses, and swift transition into complex clinical learning environments. These challenges, coupled with the realities of a strained nursing educator workforce, are potential threats to learning environments that are supportive of students’ academic tenacity and social belonging. While a clear link between nursing students’ positive sense of belonging and their success in clinical learning environments has been established, there is scant research looking at the impact of students’ self-perception of belonging in academic nursing education contexts. The aim of this project was to explore the student experience of belongingness in an academic nursing education setting. A qualitative approach influenced by phenomenology and phenomenological analysis was used. Seventeen students from an accelerated Bachelor of Science in Nursing program volunteered as participants. Individual, semi-structured interviews were conducted with each participant. Three central themes emerged from the interviews: (1) Belongingness matters to students; (2) Belongingness is constructed and understood by the individual; and (3) Belongingness is relational and contextually situated. Additionally, participants described a number of experiences that they believe enhanced or facilitated belongingness. This study adds to the literature by confirming that accelerated nursing students perceive social belonging as important in supporting their motivation, well-being, and confidence in their developing nursing identity in academic, as well as clinical, education settings.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".