P080 Self-management individualised learning environment in rheumatoid arthritis
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".