Bibliographic record
Abstract
Virtually, no published research is available on the relationship between employing newly graduated nurses (NGNs) in the emergency department (ED) and the advancing of nursing practice and the optimization of patient care outcomes. Traditionally, nurses hired into these practice areas have required advanced skills in clinical assessment and experience with a variety of situations that were assumed to offer them a framework by which they could recognize and respond to potentially life-threatening changes in a patient's status. This qualitative study explored the issues of integrating NGNs into the ED. Findings clearly established the challenges to integrating NGNs into this practice context. The intersection of variables included a low level of clinical predictability accompanied by high acuity; an increased level of practitioner autonomy combined with high levels of risk when applying decision making to patient outcomes; and the potential for devolution of professional identity in the face of highly intense, morally conflicted, and socially nuanced care situations.
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 | 0.019 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.026 | 0.045 |
| Insufficient payload (model declined to judge) | 0.026 | 0.047 |
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".