New Nurses’ Perceptions on Transition to Practice: A Thematic Analysis
Bibliographic record
Abstract
BACKGROUND: New nurses' transition to the workforce is often described as challenging and stressful. Concerns over this transition to practice are well documented, with the hypothesis that transition experiences influence the retention of new nurses in the workforce and profession. METHODS: = 217) to assess new nurse transition in the province of Ontario, Canada, an open-ended item was included to solicit specific examples of the transition experience. The comments underwent thematic analysis to identify the facilitators and barriers of transition to practice for new nurses. RESULTS: Comments were provided by 196 respondents. Three facilitator themes (supportive teams; feeling accepted, confident, and prepared; new graduate guarantee) and four barrier themes (feeling unprepared; discouraging realities and unsupportive cultures; lacking confidence/feeling unsure; false hope) to new nurse transition emerged. CONCLUSIONS: Concerns of nursing shortages are heightened in the current COVID-19 pandemic, reinforcing the priority of retaining new nurses in the workforce. The reported themes offer insight into the contribution of a supportive work environment to new nurses' transition. The recommendations focus on aspects of supportive environments and educational strategies, including final practicums, to assist nursing students' development of self-efficacy and preparation for the workplace.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".