Nurse Managers and Hospital Nurse Educators’ Views related to the Knowledge, Skill and Attitude Requirements of Newly Licensed Nurses in the Las Vegas and Salt Lake Valleys
Bibliographic record
Abstract
Background: In 2017, approximately 230,569 new Registered Nurses (RNs) were licensed in the United States. Of these, over a quarter will leave their first position in less than a year. While 90% of academic leaders feel nursing graduates are ready for practice, only 10% of clinical leaders agree. Recent changes in health care, and an intensifying theory-practice gap hint that newly licensed nurses (NLNs) may not be equipped for today’s workplace. Objective: This qualitative project asked, “What do nurse managers and hospital educators perceive as required knowledge, skills and attitudes (KSAs) for NLNs to ensure successful and safe orientation or residency?” Methods: Semi-structured interviews were conducted with twelve nurse managers and hospital- based nurse educators responsible for orienting NLNs. Interviews were conducted between October 2017 and January 2018 in the Las Vegas, Nevada and Salt Lake City, Utah nursing markets. Results: Ten themes emerged from the project. Among them “readiness to learn,” “customer service,” “physical assessment skills” and “empowerment” ranked highest. Understanding the KSAs hiring nurse managers felt NLNs should possess may help academia better prepare new nurses for today’s work environment. Conclusion: Colleges of nursing and facility partners need to communicate more frequently to ensure graduates leave school prepared to enter the workforce with the knowledge and skills relevant to the current healthcare environment. Nursing is a science, and an art. Have we sacrificed the art of nursing to focus on the science only? Increased focus on the art of nursing may help the NLN in essential skill areas for today’s work environment.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".