A Case Study of New Nurses’ Transition from Education to Rural Practice in Times of Adversity
Bibliographic record
Abstract
The transition of new nurses from training to employment in rural practice can be difficult in the best of times. The COVID-19 pandemic amplified challenges in supporting new nurses transitioning from education to employment. Drawing together Benner's novice-to-expert model and the concept of human flourishing, this article reports on research that explored new nurses' experiences transitioning from training to employment in rural nursing during the initial years of the COVID-19 pandemic, using case study methodology combining an online recruitment survey and in-depth semi-structured interviews. Participants identified a lack of on-the-job training and mentorship, feeling unprepared for the acuity of patients and concerns about patient safety, feeling unprepared for leadership roles, feeling unsupported by management, feeling fatigued and anxious, and a lack of optimism about the future of rural health care. On the positive side, participants reported valuing social connections and teamwork, gratitude from patients, and a sense of community, as well as increasing competency at work. Their stories and self-rated flourishing revealed both strengths and challenges in transitioning to practice in rural settings during times of adversity. This research can inform theories of nursing development as well as policies and practices that support new nurses to thrive in rural contexts.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".