Rural transition to practice: A phenomenological analysis of the new graduate nurses’ experience
Bibliographic record
Abstract
Objective/Background: The nursing shortage uniquely impacts rural communities as staffing issues often result in emergency department closures and leave communities without adequate healthcare. One contributing factor in this crisis is difficulty recruiting and retaining new graduate nurses (NGN) rurally. Improving transition to practice for NGNs is a potential solution to this problem. This study explores the new graduate nurses’ lived experience when transitioning to rural nursing practice.Methods: A descriptive phenomenological approach was used. Seven participants completed virtual surveys and virtual, semi-structured focus groups exploring the new graduate nurses transition to practice experience, underpinned by Patricia Benner’s From Novice to Expert model. Transcripts were analysed using thematic concept mapping.Results: Three themes were derived across the stages of transition to practice: education, mentorship and both intrinsic and extrinsic expectations on NGNs. Each phase in the first two years of practice had unique characteristics, most significantly, a six-month delay occurs to accommodate acquisition of non-nursing skills, which deviates from Benner’s model.Conclusions: This research emphasizes the importance of supporting NGNs during the first two years of transition to rural practice. The complex role of the rurally practicing registered nurse requires approximately six months more time than what is described in Benner’s model to develop competence. A focus on nursing education that begins at the undergraduate level and continues into practice is required. A shift to focus on supporting the wellbeing of the NGN is a key intervention; as well as improving mentorship and management support through education and policy change.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| 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".