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Record W4411395469 · doi:10.1016/j.ard.2025.05.581

POS0194-PARE USE OF E-LEARNING IMPROVES KNOWLEDGE AND CONFIDENCE TO MANAGE PAIN AND FLARES IN RA

2025· article· en· W4411395469 on OpenAlexaff
Alysia Bosworth, IA Scott

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2025
Typearticle
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsArthritis Society
Fundersnot available
KeywordsMedicineConfidence intervalMedical physicsFamily medicineInternal medicine

Abstract

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Background: An often insufficient aspect of care in people with inflammatory arthritis (IA) is empowering patients to acquire a good understanding of their disease and building their ability to deal with the practical, physical and psychological impacts of it. The ability to self-manage in IA represents an essential component of care that goes beyond drug therapy and in July 2021, a EULAR Taskforce published evidence-based Recommendations for Self-Management strategies in patients with IA. At the end of 2021 NRAS launched an e-learning programme, SMILE-RA, to address the needs of people with RA to learn to self-manage well. The module on Managing Pain & Flares was developed in partnership with the rheumatology team at MPFT. Objectives: The aims of this service evaluation were to assess: •patients' knowledge and confidence at self-managing their arthritis pain and flares before and after module completion •the burden of pain on patients' lives •the extent to which patients used module suggestions Methods: To address the above issues, NRAS and Dr. Ian Scott with members of the MDT at MPFT, who co-produced this module with NRAS, collaborated to develop a survey which was sent to 500 people with RA who had completed the module on Managing Pain and Flares within SMILE-RA on 25 th March 2024. Reminders to complete the survey were sent and it closed on 12 th May. NRAS received 134 completed surveys representing a 26.8% response rate which is high. People completing the survey may have completed the module at any time since 2021. Some people may have completed the module more than once. NRAS set learning objectives at the start of each module and measure learning outcomes at the end in each module within SMILE-RA. Learning objectives are generally met with scores between 92-100% for all modules. Results: Most participants were aged between 41-60 years (31%), and 61-80 years (63%). As expected, the majority were female (83%) and of white British ethnicity (91%). 96% had a diagnosis of RA with the remainder reporting they had other another type of inflammatory rheumatic disease. Most had 1-5 years (37%) or over 10 years (40%) since their arthritis diagnosis; the remainder had less than one year (14%) and 6-10 years (10%). Knowledge and Confidence Before and After the Module: There was a substantial increase in levels of knowledge about pain and flares following undertaking the module. 35.8% rated themselves as being "very" or "fairly" knowledgeable about pain before completing the module, increasing to 75.4% after completing the module. For flares these results were 29.9%, rising to 68.7%. Similar findings were seen for confidence at managing pain and flares. 28.4% rated themselves as "very" or "fairly" confident at managing their pain before completing the module, rising to 59.7% after module completion. For flares, these results were 23.1% and 50.0%. In addition to the above results, we also asked participants how likely they were to try some of the non-pharmacological suggestions to manage pain and flares in the survey and 43.3% and 41.8% said they were "very likely" or "likely" for pain and flares, respectively. Of those people experiencing pain in the past 3 months, many took pain medicines in the past month, with 62% reporting using paracetamol, 24% Co-codamol/Co-dydramol, 32% oral Non-Steroidal Anti-Inflammatories (NSAIDs), 18% topical NSAIDs, 5% Tramadol, 2% pain patches, 2% Gabapentin or Pregabalin, and 18% other over the counter pain medicines. Only 13% reported not taking any pain medicines in the last month. Many participants had also found non-medication methods to manage pain helpful, including heat therapy (64%), cold therapy (23%), pacing (41%), stress management (25%), distraction (23%), and relaxation (31%). Conclusion: This evaluation of the NRAS SMILE Managing Pain and Flares module has three key findings. First, it highlights the ongoing impact of chronic pain in people with rheumatoid arthritis, with two thirds of people giving module feedback having chronic pain, which was "high impact" in one third of people. Second, it demonstrates that people with RA use a broad range of methods to self-manage their pain, spanning analgesics (particularly paracetamol), and non-drug approaches (with two thirds finding exercise and heat therapy helpful, and one half finding staying positive helpful). Third, it shows that many people felt that their knowledge about their pain and flares, and confidence in managing these were substantially enhanced through undertaking the module. Overall, these findings indicate that the module has an important role to play in enabling people with RA to better self-manage their arthritis related pain and flares which has cost-saving implications for the NHS. REFERENCES: NIL . Acknowledgements: Acknowledgement to the multidisciplinary team at the MPFT and the Haywood Hospital, Stoke-on-Trent. Disclosure of Interests: Ailsa Bosworth NRAS has received educational grants from a number of pharmaceutical companies but not in relation to this abstract, Ian Scott: None declared. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0370.006

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.

Opus teacher head0.041
GPT teacher head0.360
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
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