Development and Implementation of MyPainHub, a Web-Based Resource for People With Musculoskeletal Conditions and Their Health Care Professionals: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Musculoskeletal conditions, including low back pain (LBP), neck pain, and knee osteoarthritis, are the greatest contributors to years lived with disability worldwide. Resources aiming to aid both patients and health care professionals (HCPs) exist but are poorly implemented and adopted. OBJECTIVE: We aimed to develop and implement MyPainHub, an evidence-based web-based resource designed to provide comprehensive, credible and accessible information for people with, and HCPs who manage, common musculoskeletal conditions. METHODS: This mixed methods study adhered to the New South Wales Translational Research Framework and was evaluated against the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. Consultation with key stakeholders (patients, HCPs, researchers, industry, consumer groups, and website developers) informed content, design, features, and functionality. Development then aimed to meet the identified need for a "one-stop shop"-a central location for information about common musculoskeletal conditions tailored to a person's condition and risk of poor outcomes. MyPainHub was then developed through an iterative process and implementation strategies were tailored to different health care settings. Quantitative and qualitative evaluation occurred with patients and HCPs. RESULTS: In total, 127 stakeholders participated in the development phase; initial consultation with them led to embedding 2 validated screening tools (the Short Form Örebro Musculoskeletal Pain Screening Questionnaire and the Keele STarT MSK tool) in MyPainHub to guide information tailoring for patients based on risk of poor outcomes. Development occurred in parallel and feedback from stakeholders informed design and content including structure, functionality, and phrasing and images to use to emphasize key points. Consultation resulted in information for patients being categorized using key guideline-based messages (general information, your pathway, exercise, and imaging) while information for clinicians was categorized into assessment, management, and prognosis. Implementation occurred in different health care settings with the most effective strategies being interactive education via webinars and workshops. The evaluation phase involved web-based questionnaires (patients: n=44; HCPs: n=29) and focus groups (patients: n=6; HCPs: n=6). Patients and HCPs found MyPainHub user-friendly, acceptable, credible, and potentially able to support self-management. Patient participants identified areas for improvement such as including more specific information on preventative measures and pain relief options. Despite positive feedback, only 35% (10/29) of HCPs used MyPainHub with their patients. HCP participants identified challenges including insufficient training and lack of familiarity with using web-based resources in existing clinical workflows. Following implementation, the information contained on MyPainHub changed knowledge and practice for some patients and HCPs. CONCLUSIONS: Following extensive and iterative stakeholder engagement, MyPainHub was developed as an evidence-based web-based resource and perceived by patients and HCPs as user-friendly, credible, and acceptable. Active implementation strategies are required for adoption and implementation and greater training focusing on strategies to implement MyPainHub into clinical practice may be necessary. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12619000871145; https://tinyurl.com/438kkyt3.
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 | 0,053 | 0,046 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».