Bibliographic record
Abstract
ColinPowell said: "Organizations don't really accomplish anything.Plans don't accomplish anything either.Theories of management don't much matter.Endeavours succeed or fail because of the people involved.Only by attracting the best people will you accomplish great deeds" (1996).How to be an employer of choice for the brightest graduates and the best, expert, specialty nurses is the challenge for nurse leaders of the new millennium.The nursing shortage has arrived.With it comes a multitude of factors that have stretched the health care system and challenged all health care employees.It may read as hopeless, but do not despair!The environment for nursing staff has become competitive.This is not a bad thing.It simply requires that leaders of organizations, divisions and services ensure that their environment is a great place to work.This article may help you to rethink your strategies in order to become an employer of choice.The nursing shortage is real.We face the 'retirement wave' as the percentage of RNs under 30 years of age shrinks at a constant rate from 25% of the nursing population in 1980 to 9% in 1996, and the average age of RNs increases from 40.3 in 1980 to 44.3 in 1996.Concurrently, there is declining enrollment in nursing schools as more people choose professions with increased wages and better working conditions.Specialty nurses are becoming an elusive breed where it can take as long as 90 days to fill vacancies in areas such as emergency, oncology, intensive care and
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.054 | 0.012 |
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".