P080 Self-management individualised learning environment in rheumatoid arthritis
Notice bibliographique
Résumé
Abstract Background/Aims An important but insufficient aspect of care in people with inflammatory arthritis (IA) is empowering them to acquire a good understanding of their disease and build their ability to deal effectively with the practical, physical and psychological impacts of it. This extends beyond drug therapy and emphasises the ability to self-manage, with the right support, as an essential component of care. Good self-efficacy and coping skills benefit and reduce health and financial burden to the individual as well as the health service, benefitting society overall. Provision of excellent supported self-management education is at the heart of what NRAS does and it was due to the difficulty of getting Commissioners to fund our group self-management that led to our deciding to build an e-learning programme to replace our 6- week face-to-face programme. Methods In 2019 with initial funding in place, we partnered with an e-learning platform provider who would help us realise our goal of developing a state-of-the art e-learning experience in a modular format for people with RA. We wanted the programme to be 1) simple to use; 2)interactive; 3)engaging; 4)able to measure impact through learning objectives and use of a validated patient reported outcome measure. The programme also had to be integrable with our Salesforce database within NRAS enabling us to target resources to individuals, driven by identified need. Results The pandemic delayed progress, however, we launched with 4 modules on 17th September, 2021. The four modules comprise: Foundation Module covering the importance of self-management which has the RA Impact of Disease PROM embedded; Newly Diagnosed; Meet the Team and Managing Pain and Flares. A fifth module on Medicines in RA will be launched by end 2021. SMILE meets NICE Quality Statement 3, against which teams are audited, and aligns with EULAR Recommendations for implementation of self-management strategies in IA. Since launch 3 weeks ago, nearly 300 people have registered. Well over 100 baseline RAID PROMs have been completed and we are starting to collect valuable data which will enable us to target more resources where they are needed. Conclusion SMILE-RA provides patients with a unique, engaging educational resource which they can watch and watch again with their family. It is also a useful resource for AHPs new to rheumatology. Further modules will be added in 2022 and beyond. Many modules on a wide range of topics are planned and so this is an on-going project which has input from health professionals and people with lived experience at its heart. Patients and HCPs have welcomed this new resource at a time when the rheumatology workforce is in crisis. We will be presenting more data in Q1 2022 which will be available for congress. Disclosure A.M. Bosworth: 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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,034 | 0,007 |
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 ».