New Graduate Nurses Navigating Entry to Practice in the Covid-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: The Covid-19 pandemic has significantly impacted organizational life for nurses, with known physical and psychological impacts. New graduate nurses are a subset of nurses with unique needs and challenges as they transition into their registered nurse roles. However, this subset of nurses has yet to be explored in the context of the Covid-19 pandemic. PURPOSE: To explore the experiences of new graduate nurses entering the profession in Ontario, Canada, during the Covid-19 pandemic approximately one year after entering the profession. METHODS: Thorne's interpretive description method was utilized. FINDINGS: Participants felt ill prepared to enter the profession and were cognizant of the various challenges facing the nursing profession, and how these pre-existing challenges were exacerbated by the pandemic. They acknowledged the need to protect themselves against burnout and poor mental health, and as such, made calculated early career decisions - demonstrating strong socio-political knowing. Half of the participants had already left their first nursing job; citing unmet orientation, mental health, and wellbeing needs. However, all participants were steadfast in remaining in the nursing profession. CONCLUSIONS: Second entry new graduate nurses remain a unique subset of nurses that require more scholarly attention as their transition experiences may differ from the traditional trajectory of new graduate nurses.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".