Fit for work? Evaluation of a workshop for rheumatology teams
Bibliographic record
Abstract
BACKGROUND: People with rheumatoid arthritis (RA) may rapidly cease work prematurely due to ill-health. A recent survey noted that a quarter of respondents with RA experienced job loss within a year of diagnosis and 50% stopped work within 6 years. AIMS: To develop and pilot workshops to increase the knowledge, skills and confidence of rheumatology team members to support work-related issues in outpatient clinics. METHODS: A 3-h interactive workshop, informed by rheumatology experts and the Royal College of General Practitioners (RCGP) National Education Programme (NEP) about work and health, was developed to address both knowledge and skills in the management of health and work consultations in an outpatient setting. Questionnaires were developed for use pre- and immediately post-workshop, with questions that focused on the confidence of delegates in managing these discussions and the importance they placed upon them. RESULTS: Ninety-nine participants attended five workshops throughout the UK between 2013 and 2104. Seventy-three per cent (72) completed the post-workshop questionnaire. Eighty-nine per cent found the workshop useful or very useful, 88% found it relevant or very relevant and 79% responded that it had an impact or a considerable impact on their practice. Wilcoxon matched pairs signed rank tests were carried out that showed an overall increase in confidence after training. CONCLUSIONS: The results suggest that the workshop was both relevant and useful to participants and had an impact on their practice. This was true for all specialities. The workshops also highlighted participants' desire to understand how to use the 'fit note' to enhance their patient management.
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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 | 0.033 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".