Bibliographic record
Abstract
Background and objective: A shortage of nearly half a million registered nurses threatened to disrupt health care services by the year 2020 as approximately one million registered nurses, born during the baby boom generation, were projected to retire. This predicted shortage would greatly affect the quality of patient care delivery. The predicted crisis drew the attention of stakeholders across the nation.Methods: This article summarizes strategies implemented to meet the growing demand for registered nurses by various agencies and stakeholders, the result of those efforts, and future challenges currently facing the profession.Results: Interventions resulted in a renewed interest in the profession of nursing. The total number of graduates from ADN and BSN programs more than doubled from 2002 to 2012. The number of master’s and doctoral program graduates more than tripled. Full time employment of registered nurses increased from 2.1 million in 2001 to 3.2 million in 2015.Future challenges and implications: Limited employment opportunities for new graduate nurses as hiring has slowed, concern over the quality of nursing education across all program types, and the need for ongoing assessment and implementation of guidelines permitting nurses to practice to the full scope of their educational preparation and capabilities during an era of continued health care reform, are among the challenges faced by the profession. In addition, delivery of safe and effective care to meet the needs of an aging population will present many challenges in the future.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".