A Longitudinal View of Perceptions of Entering Nursing Practice During the COVID-19 Pandemic
Bibliographic record
Abstract
Background The COVID-19 pandemic significantly changed the landscape of health care and transition to practice for new graduates. The purpose of this pilot study was to explore the effects of the pandemic on the first-year experience of new nurses. Method A longitudinal, observational, descriptive study design was used. One hundred eighteen survey links were sent to new bachelor of science in nursing graduates from June 2020 to May 2021, with 56 responses to the first survey. Results Participants indicated the COVID-19 pandemic negatively affected the new graduate experience, resulted in concern for personal health and safety, and negatively altered preparation for the first year in practice. However, desire to be a nurse and view of nursing remained positive. Conclusion The first year in practice is stressful and challenging. The pandemic posed additional challenges to employers and new graduates. Future research should explore the long-term impact of the pandemic on an already strained nursing workforce. [ J Contin Educ Nurs . 2022;53(6):256–263.]
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".