The value, impact and role of nurses in rheumatology outpatient care: Critical review of the literature
Bibliographic record
Abstract
BACKGROUND: As rheumatology nurses make substantial contributions to intensive management programmes following 'treat to target' principles of people with rheumatoid arthritis (RA), there is a need to understand the impacts of their involvement. A structured literature review was undertaken of qualitative studies, clinical trials and observational studies to assess the impacts of rheumatology nurses on clinical outcomes and the experiences of patients with RA and to examine the skills and training of the nurses involved. METHOD: A structured literature review was conducted to examine the value, impact and professional role of nurses in RA management. RESULTS: The literature search identified 657 publications, and 20 of them were included comprising: seven qualitative studies (242 patients), nine trials (a total of 2,440 patients) and four observational studies (1,234 patients). In clinical trials, nurses achieved similar patient clinical outcomes to doctors, and nurses also enhanced patients' satisfaction of received care and self-efficacy. In the qualitative studies reviewed, the nurses increased patients' knowledge and promoted their self-management. The observational studies studied examined found that nursing care led to improved patients' global functioning. The nurses in the various studies had a wide range of titles, experiences and training. DISCUSSION: Our structured literature review provides strong evidence that rheumatology nurses are effective in delivering care for RA patients. However, their titles, experience and training were highly variable. CONCLUSION: There is a convincing case to maintain and extend the role of nurses in managing RA, but further work is needed on standardisation of their titles and training.
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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 | 0.019 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".