150 What is the value, impact and role of nurses in rheumatology outpatient care?
Bibliographic record
Abstract
Background: Following the development of an intensive management programme delivered by specialist nurses and other clinicians to patients with moderate rheumatoid arthritis (RA) (TITRATE), we have identified complexities in its potential implementation. We therefore: (i) systematically reviewed the evidence to determine the extent to which rheumatology nurses benefit RA patient outcomes in trials, qualitative studies and observational studies; (ii) assessed the size of the UK NHS rheumatology nurse community; and (iii) established the experience and background of nurses and other healthcare staff delivering intensive treatment for the TITRATE programme. Methods: The systematic review involved a search in Medline using the terms ‘nursing’ and ‘rheumatoid arthritis’, limited to English publications from January 2000 to August 2018. Rheumatology nurse numbers were obtained from surveys by the National Audit Office and National Clinical Audits. The TITRATE dataset provided professional background, years of RA experience and professional titles. Results: Our systematic review identified 653 publications: 48 were selected for detailed review; and 16 included. They comprised 7 trials (1,894 patients), 6 qualitative studies (121 patients) and 3 observational studies (1,043 patients). We found: trials show nurses achieve similar clinical outcomes to doctors whilst also enhancing patient satisfaction and self-efficacy; qualitative studies show nurses increase knowledge and promote self-management; and observational studies show nurses improve global assessments. National Audit Office and National Clinical Audits identified 355-377 rheumatology nurses in England with 0.64/100,000 rheumatology nurses compared to 0.85/100,000 consultant rheumatologists. From 2014-17 the TITRATE programme trained 100 nurses and other healthcare staff from 39 NHS Trusts (42 sites) in intensive treatments: 86 were females and 14 males; their mean age was 48 years (range 28-72). Their rheumatology experience varied: the mean duration 5 years (range 0-30); 59 had 4 years or less rheumatology experience. Their professional backgrounds varied: 85 were nurses; 8 were other health professionals; 6 had medical backgrounds in non-consultant posts; and one was an occupational therapist. There were marked differences in how they described their roles. Overall, 48 different titles were recorded. For example, nurses called themselves rheumatology nurse, rheumatology nurse specialist, rheumatology clinical nurse specialist and rheumatology nurse practitioner with marked variation in their seniority ranging from grade 5 nurses to a modern matron. Conclusion: Rheumatology nurses are effective in the delivery of care with substantial numbers in posts. However, there are fewer nurses than consultants and they have a range of clinical experiences and titles. The latter makes their specialist role and relative seniority difficult to judge by both patients and colleagues. The delivery of intensive treatment is likely to be improved by greater standardization and training. Disclosures: R. Baggott: None. D. Scott: None. J. Sturt: None. A. Bosworth: None. L. Parker: None. S. Georgopoulou: None. H. Lempp: None.
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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 | 0.058 | 0.290 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".