Nursing Students’ Transition Experiences from Final Year Nursing Student (FYNS) to Newly Graduated Registered Nurse (NGRN) during the COVID Pandemic
Bibliographic record
Abstract
The impact of the coronavirus disease (COVID-19) pandemic on nursing education and clinical practice is underexplored.Surveys of students' readiness to practice during the pandemic showed that most felt unprepared upon graduation.To explore the experiences of final-year nursing students transitioning to newly graduated registered nurses during the COVID-19 pandemic.A thematic analysis of 14 semi-structured interviews was conducted with final-year nursing students and registered nurses who graduated between 2020 and 2024, selected by purposive sampling.Five themes were identified: (1) Theory and Practice Gaps (2) learning environment; (3) instructors, faculty, and staff; (4) transition facilitators; and (5) orientation and mentorship.The COVID-19 pandemic has significantly challenged nursing education, with cancelled labs and clinical hours leading to knowledge deficits, unpreparedness, and increased stress among students.Reducing graduation requirements raised concerns about workforce readiness, experience, and critical thinking abilities.Undergraduate employment aided RN preparation, and coping strategies included peer support and work-life balance.Successful transition required comprehensive orientation and mentorship programs.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".