Exploring the Experiences and Perspectives of new Graduate Nurses on the Push-Pull Factors of Nursing Workforce Crisis Post COVID-19
Bibliographic record
Abstract
BackgroundThe 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.DesignQualitative, descriptiveMethodsFifteen 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.ResultsParticipants 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.ConclusionNGNs 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".