Bibliographic record
Abstract
BACKGROUND/RATIONALE: The number of individuals with chronic illness is growing at an astonishing rate because of the rapid aging of the population and the increased longevity of persons with chronic conditions. Nurses in clinical nurse specialist (CNS) roles are well positioned and ideally suited to meet the needs of a growing population with chronic diseases; yet, to date, there has been no critical review of the CNS in chronic diseases. PURPOSE/OBJECTIVES: This article provides a critical review of the literature in order to better define and understand the CNS related to patients living with chronic illnesses (cardiovascular and oncology). DESCRIPTION OF THE PROJECT/INNOVATION: Using the guidelines of DiCenso et al (2005) for evaluating health services interventions, the literature was appraised in order to identify the characteristics of CNS roles, and the strengths and limitations of research about the effectiveness of CNS in chronic disease management. IMPLICATIONS: Clinical nurse specialists with master's-level preparation provided high-quality and cost-effective care to patients with chronic diseases. The CNSs had a positive impact on patient, family, and healthcare team outcomes. Further evaluation of the CNS role in the research domain of practice is recommended.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".