Exploring the Experiences and Perspectives of new Graduate Nurses on the Push-Pull Factors of Nursing Workforce Crisis Post COVID-19
Bibliographic record
Abstract
Background The aim of this study is to explore the practice experiences of new graduate nurses (NGNs) in publicly funded acute healthcare settings in the Greater Toronto Area, their perspectives on the determinants of their desire to stay or leave the nursing profession, and to identify action-oriented strategies to promote retention of NGNs. Design Qualitative, descriptive Methods Fifteen NGNs participated in focus group sessions, where a semi-structured interview guide was created to generate discussion on NGNs’ lived and professional experiences. We utilised the Social Ecological and Intersectionality frameworks to guide data analysis with an emphasis on social identities, power relationships, and the personal, interpersonal, organizational, and structural determinants of nursing retention. Results Participants contextualized their major challenges within four professional development phases: 1.) accessible nursing education and practicum placement; 2) preparedness, orientation and mentorship during entry to practice; 3) navigating transition to independent practice and multi-level structural violence; 3.1) retention strategies; and 4) perspectives on professional trajectory for NGNs. Conclusion NGNs experience major challenges throughout their nursing education and career. The study findings indicate that further research and systemic reform is essential to support, develop, and retain nursing leaders in the acute care setting. Furthermore, the findings can inform the development of evidence-based nursing curriculum reform.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".