150 What is the value, impact and role of nurses in rheumatology outpatient care?
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,058 | 0,290 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,008 | 0,008 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».