091 Insights into issues related to job loss in patients with rheumatoid arthritis: a UK national survey
Bibliographic record
Abstract
Background: People with chronic musculoskeletal health condition(s), including rheumatoid arthritis (RA) continue to face challenges to remain in work, compared to healthy peers. Understanding why people have to stop working and possible issues people with RA face when trying to return to work will guide future interventions. Methods: An online survey was sent to National Rheumatoid Arthritis Society (NRAS) members and distributed to non-members via social media tools. Questions about reasons to stop working and regaining employment were asked to those who were no longer working. Participants were also asked how serious specific issues in their last job were. A similar question was asked to those currently employed. Results: Of those who completed the survey, 322/1222 (26%) people reported not being in paid employment, of which 42% stopped working because of their arthritis and 33% retired early because of their arthritis. The three most common reported reasons for leaving work were; unable to carry out duties because of physical limitations (63%), time off sick (38%) and fatigue affecting ability to work (65%). Prior to stopping work, less than 50% of respondents were given support to make changes to their working environment, including flexible working, working fewer hours or being provided with special equipment in their last job. Compared to those in current employment, a higher proportion of those not in work, reported more serious issues related to their arthritis in their last job, especially issues on having time off when having a flare, lack of support from employer/line manager and lack of understanding from colleagues (Table 1). 37% of those not working said they were willing to regain employment. 20% (including those who had retired early) said they had attempted to regain employment. Approximately two thirds (67%) of people said they declare their RA when applying for jobs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".