P073 Making It Work™ - UK: adaptation, development, and user-testing of a Canadian programme to support people working with musculoskeletal conditions in the UK
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».