Bibliographic record
Abstract
For newly graduated nurses (NGNs), the characteristically challenging and dynamic period of transition from student to professional practitioner is being further strained by global crises and the uncertainty and insecurity they motivate, health care systems and institutional restructuring, and extreme workload burdens. A novel approach to aiding the transition of NGNs is detailed in this article, culminating in the offering of an inclusive framework of potential strategies aimed at supporting NGNs and those who lead, manage, and educate them. This approach outlines strategies of support deliverable by both centralized and local means and acknowledging contemporary needs such as workload burdens and generationally-sensitive employee needs. Nursing The Future is a platform that uniquely situates an evidence-based, grassroots-driven response to the needs of NGNs, while encouraging collaborative partnering of health care institutions with governmental, professional, and regional advanced education bodies. This is the second article in a 2-part series that builds on the historical and developmental intents of Nursing The Future as an organization and outlines how evidence-informed, creative, and affordable grassroots-driven supports may be offered to NGNs for the purpose of sustaining and advancing our future nurse professionals.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.261 | 0.201 |
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".