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P103 Evaluation of an online learning module from the National Rheumatoid Arthritis Society to support the self-management of pain and flares in people with rheumatoid arthritis

2025· article· en· W4409898728 on OpenAlexaff
Ian C. Scott, Sarah Ryan, Gillian Levey, Martin J. Thomas, Samantha Hider, Ailsa Bosworth

Bibliographic record

VenueLara D. Veeken · 2025
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsArthritis Society
FundersUCB PharmaNational Institute for Health Research Applied Research Collaboration WestKeele UniversityArts and Humanities Research CouncilVersus ArthritisMedical Research CouncilNational Institute for Health and Care Research
KeywordsRheumatoid arthritisMedicinePain managementPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background/Aims Many people with rheumatoid arthritis (RA) have chronic pain and arthritis flares. Supporting their self-management of these disease impacts is therefore crucial to improving their quality of life. To address this, the National RA Society (NRAS) co-developed an online learning module for people with RA on managing pain and flares (embedded in their “SMILE-RA” e-learning programme) in collaboration with the Midlands Partnership University NHS Foundation Trust multidisciplinary rheumatology team. The aim of this service evaluation was to assess patients’ knowledge and confidence at self-managing arthritis pain and flares pre- and post-module completion. It also examined the burden of pain on patients’ lives/the extent to which patients used module suggestions. Methods The module was launched in September 2021. A survey was sent via email in March 2024 (available until May 2024) to the 500 people completing the module consenting to contact for feedback. Survey questions covered: (1) demographics; (2) pain experience/management; (3) knowledge/confidence on managing pain/flares (Likert-type responses); (4) likelihood of trying module suggestions (Likert-type responses); (5) free-text feedback. Descriptive statistics summarised survey responses as proportions/means (with standard deviations [SD]) as appropriate. Fisher’s exact tests compared Likert-type responses for knowledge and confidence pre-/post-module. Results Demographic/Arthritis Characteristics: 134 patients completed the survey (27% response rate). 95% reported having RA (5% reported other inflammatory arthritis types). Most (63%) were aged 61-80 years and female (83%). Pain Experience/Management: 98% experienced pain in the past 3 months, present “every/most days” in 63%. Approximately one-third (36%) reported “high impact” chronic pain. Of those with pain in the past 3 months, 87% used analgesics in the last month. Many found non-drug pain care helpful, particularly heat therapy (64%) and exercise (61%). Knowledge/Confidence: The proportion rating themselves “very/fairly/somewhat” knowledgeable at managing pain rose from 62% pre-module to 95% post-module (P = 0.01) and for managing flares from 52% pre-module to 93% post-module (P<0.01). For confidence at managing pain, the proportion rating themselves “very/fairly/somewhat” confident rose from 50% pre-module to 90% post-module (P<0.01) and for managing flares from 44% pre-module to 90% post-module (P<0.01). Using Module Suggestions: 79% and 78% reported they were “very likely/likely/fairly likely” to try module suggestions to manage their pain and flares, respectively. Free-Text Feedback: Key themes were increasing knowledge (e.g. “This is really useful because I don’t feel the NHS gave me enough information and I need something I can trust on the internet”) and self-management (e.g. “It made me feel confident that I could manage on-going problems and flares and rely less on medication”). Conclusion This service evaluation highlights the impact of chronic pain on the lives of people with RA and demonstrates the benefits of this multidisciplinary team-developed online educational resource at improving patient’s knowledge and confidence to self-manage pain and flares. Disclosure I.C. Scott: Grants/research support; National Institute for Health and Care Research (NIHR) Advanced Research Fellowship [NIHR300826]. S. Ryan: None. G. Levey: None. M.J. Thomas: Grants/research support; National Institute for Health and Care Research (NIHR)/Versus Arthritis. S. Hider: Honoraria; SH has received payment for lecture fees from UCB. A. Bosworth: None.

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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)0.008
metaresearch head score (Gemma)0.018
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 score0.010
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.016
GPT teacher head0.284
Teacher spread0.267 · 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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Citations2
Published2025
Admission routes1
Has abstractyes

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