P073 Making It Work™ - UK: adaptation, development, and user-testing of a Canadian programme to support people working with musculoskeletal conditions in the UK
Bibliographic record
Abstract
Abstract Background/Aims Musculoskeletal (MSK) conditions can have a substantial influence on an individual’s ability to work and are one of the most common reasons for sickness absence in the UK. People working with MSK conditions often struggle with issues like managing fatigue and stress at work, and it can be challenging to ask for and obtain job accommodations. Making it Work™ is an online self-management programme that was originally developed in Canada to support people with inflammatory arthritis with these challenges. Within an RCT, the programme was shown to effectively reduce long-term sickness absence. This project aimed to adapt the Making it Work™ programme to make it suitable for use within the UK and for people working with a wider range of MSK conditions, including inflammatory arthritis, osteoarthritis and fibromyalgia. Methods Fifteen interviews were conducted with people living with non-inflammatory musculoskeletal conditions to understand their impact on work and work transitions. Findings from the interviews, alongside focus groups and workshop with employers, HCPs and patients identified priorities and informed adaptation of the original programme. Changes were made to the content, structure, navigation, branding and design of the programme while retaining key programme content and components. Iterative feedback from patient partners and stakeholders guided these changes, which were then taken forward by an eLearning team to create the adapted programme. We sought input from patient partners with experience of working with MSK conditions to provide feedback on the programme as part of a user-testing process. This focused on understanding the acceptability, relevance, and usability of the adapted programme and on identifying and prioritising final changes. Results The adapted programme comprises five core modules combining information, activities, relaxation, and self-reflection that individuals complete on their own, at their own pace. These modules retain key content from the original programme, addressing important issues at work: fatigue, stress, communicating effectively at work, disclosure, and requesting job modifications. Changes were made within modules to ensure that case-studies, examples, and information reflected a broader spectrum of conditions and greater diversity of working situations. Programme sections were re-structured to enhance navigation and information flow, and a “self-reflection” section was added to each module, replacing in-person group meetings, and enabling full online delivery. These sections encourage individuals to revisit activities, reflect on their progress and identify areas for improvement. Twenty patient partners participated in user-testing. There was broad consensus that the adapted programme was acceptable, relevant, usable and, importantly, met an unmet need. Conclusion The online programme offers a cost-effective and accessible source of evidence-based support, aligning with national efforts to improve equity in access to support-to-work services. It also provides important lessons for the effective adaptation of resources developed in different healthcare contexts to a UK context. Disclosure R. Hollick: None. C. Ghiglieri: None. S. Anderson: None. E. Wainwright: None. D. Lacaille: None. G. Macfarlane: None. L. Morton: None.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".