Bibliographic record
Abstract
What was initiated as a directive from a provincial government in an attempt to increase the number of critical care nurses has evolved into an exciting educational opportunity for many nurses and student nurses in the year 2000. Between 1993 and 1997 there has been significant downsizing of acute care beds across Canada (Code Blue: Critical Care Nursing in Nova Scotia, 1998). At the same time patient acuity has increased, due to shorter hospital stays, and the number of nurses working full-time has decreased with the increased use of casual nurses. Several studies at both the provincial and national levels report current and future shortages of specialized nurses (emergency, critical care and perioperative). It is expected that this shortage will continue into the future, a shortage that is driven by technological advances, as well as an aging general and nursing population. Continued shortages of these acute care nurses will result in fierce competition for skilled nurses as well as aggressive recruitment and retention strategies (Code Blue: Critical Care Nursing in Nova Scotia, 1998). It is generally agreed within the nursing community that specialty nurses in critical care require a unique body of knowledge that is not acquired in a basic undergraduate nursing program (Fitzsimmons, Hadley, & Shively, 1999). This specialized knowledge can be gained informally through experience; however, it is largely developed in additional formal education programs. The purpose of this article is to outline a strategy for the delivery of specialty education at three educational levels in acute care nursing with three streams: emergency, critical care and perioperative nursing. This clinical major option is to be delivered in partnership among the Queen Elizabeth Hospital II, the Health Science Centre and Dalhousie University School of Nursing, Halifax, Nova Scotia, Canada. This model of offering specialty education in university preparation could be a template for preparing nurses in the new millennium.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.743 | 0.389 |
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".