What do users want from rheumatology telephone advice lines? A cross-sectional survey with the National Rheumatoid Arthritis Society
Bibliographic record
Abstract
Abstract Objectives Telephone advice lines are a key component of National Health Service (NHS) rheumatology services and increased demand poses challenges for users and service providers. To explore the experiences of people using these services we undertook an evaluation survey with the National Rheumatoid Arthritis Society (NRAS). Methods An online survey, co-designed with people with lived experience, was distributed by NRAS between August and September 2024. The survey collected data on respondent demographics, reasons for contacting advice line services, experiences using the advice line and how services could be improved. Results A total of 1423 participants completed the survey. The majority were female [n = 1338 (94%)], of White British ethnicity [n = 1455 (95%)], had rheumatoid arthritis [n = 1288 (91%)], with a disease duration of >6 years [n = 975 (68%)] and were 61–80 years of age [n = 849 (56%)]. Most services were automated [n = 1273 (85%)], although participants would prefer to speak to someone directly [n = 889 (59%)]. The main reasons for contacting advice lines were experiencing a flare [n = 946 (66%)], pain [n = 876 (61%)] and medication concerns [n = 863 (61%)]. Most participants found the advice to be ‘helpful to very helpful’ [n = 847 (59%)] and were ‘confident to very confident’ [n = 866 (61%)] they could implement the advice given. A total of 839 (56%) calls were returned within 48 hours. There were 665 free-text responses on how telephone advice line services could be improved that focused on three main areas: increasing availability, improving response times and having more staff to deliver advice line support. Conclusion The increasing demand for NHS rheumatology telephone advice line services requires a redesign of current systems to maximize accessibility and manage user expectations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".