Development and Implementation of MyPainHub, a Web-Based Resource for People With Musculoskeletal Conditions and Their Health Care Professionals: Mixed Methods Study
Bibliographic record
Abstract
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.
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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 | 0.053 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".