P085 NRAS New2RA Right Start for people newly/recently diagnosed with RA
Notice bibliographique
Résumé
Abstract Background/Aims The National Rheumatoid Arthritis Society (NRAS) follows best practice, evidence-based standards in all we do. Whilst huge strides have been made in the diagnosis and treatment of Rheumatoid Arthritis (RA), the impact on quality of life can be significant and for many, RA remains hard to come to terms with. Anxiety and depression are frequent co-morbidities seen in RA, particularly in the early stages. This can impede people’s ability to acquire knowledge about their disease together with supported self-management skills and coping strategies. The aim of Right Start is to improve outcomes of the newly/recently diagnosed with RA through a framework of emotional, educational and peer support, and access to high quality supported self-management resources tailored to individual need. Methods Right Start involves a simple, 4-step process: • a call (up to 1 hr) with a member of our helpline. • 1:1 peer support from trained volunteers with RA, 24/7 online community support • a tailored package of hard copy information with e-links of interest sent by post • further follow up available by helpline and individual peer support Right Start enables health professionals to meet their responsibilities against NICE Quality Standard 33, Statement 3, on which they are audited through National Early Inflammatory Arthritis Audit (NEIAA), and the EULAR Recommendations for implementation of self-management strategies in inflammatory arthritis. Results Since launch at BSR 2019, 71 rheumatology units have referred over 400 patients to this service. Approx. 1/3rd couldn't be contacted and after 3 attempts, a letter and newly diagnosed pack are sent, inviting contact at future date. Anecdotally people are highly satisfied with this service and a number of units are referring multiple patients. To gather empirical data on the impact of the service NRAS has partnered with Manchester University to undertake an Enhanced Right Start pilot in 5 UK rheumatology units involving the use of validated patient reported outcome and experience measures from the users recruited, and quantitative and qualitative data from the health professionals. The pilot is due to commence before end 2021. Meantime, referrals continue to grow. Conclusion The NHS rheumatology workforce is in crisis and Right Start enables professionals to offer evidence-based education and supported self-management resources to optimise patient care. A Nurse Consultant (West Middx.) said of the service: “I have found Right Start to be an excellent resource for people who have recently been diagnosed with RA. When time is limited time in clinic, a referral provides the patient with tailored information which complements the education I have been able to provide. Feedback from patients has been positive particularly relating to the phone calls with the NRAS helpline team and the NRAS volunteer with RA. I would definitely recommend the service.” Disclosure A.M. Bosworth: None. I. McNicol: 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 | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,083 | 0,012 |
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 ».