Effectiveness and Cost-Effectiveness of a Stepped Model of Care for Musculoskeletal Disorders: Protocol for a Multiarm Randomized Controlled Trial (Edu-First Trial)
Bibliographic record
Abstract
BACKGROUND: Musculoskeletal disorders (MSKDs) are a leading cause of pain and disability, placing a substantial burden on health care systems. Optimizing resource use through innovative interventions is essential. Evidence from randomized controlled trials suggests that not all individuals with MSKDs require ongoing follow-up with a health care provider; for many, education alone is sufficient for symptom resolution. A stepped care model, which prioritizes patient education as a first-line intervention and reserves usual care for those with persistent symptoms, may enhance health care efficiency and reduce costs. OBJECTIVE: The primary objective of this randomized controlled trial is to evaluate the effectiveness of a stepped care model compared to the 2 most common approaches for managing MSKDs: usual medical care and usual rehabilitation care. A secondary objective is to assess cost-effectiveness. METHODS: This pragmatic, noninferiority, multiarm, parallel-group randomized controlled trial will enroll 369 adults with MSKDs, randomly assigned to one of three 12-week intervention groups: stepped care, usual medical care (physician-led), or usual rehabilitation care (physiotherapist-led). Participants in the stepped care group will first complete a 6-week education program. Those with persistent symptoms after 6 weeks will receive rehabilitation interventions, while participants whose symptoms have resolved will receive no further intervention. The primary outcome is functional limitations at 24 weeks. Secondary outcomes include pain severity, health-related quality of life, pain-related fear, and pain self-efficacy, assessed at baseline and at 6, 12, and 24 weeks. Linear mixed models will be used for group comparisons, and incremental cost-effectiveness analyses will evaluate cost-effectiveness. The ethics committee of the CIUSSS-CN approved the project (#2024-2982). Findings will be shared through clinical and community platforms, peer-reviewed publications, and conference presentations. RESULTS: The Edu-First trial is funded by a project grant from the Canadian Institutes of Health Research (grant #495615). Recruitment began on January 31, 2025. As of September 2025, a total of 65 participants have been enrolled. Recruitment is expected to continue for up to 3 years, targeting approximately 10 new participants per month, and is anticipated to be completed by Winter 2028. CONCLUSIONS: We anticipate that the stepped care model will be noninferior to usual medical care and usual rehabilitation care in terms of treatment effectiveness. Furthermore, it is expected to be cost-effective by reducing reliance on expensive resources, such as provider consultations and medical investigations. By emphasizing education and self-management as the initial approach, the stepped care model may enhance access to care without compromising quality, while empowering patients to actively manage their condition. Findings from this study could inform systemic changes in MSKD care delivery, improving treatment accessibility and reducing the average cost per care episode. TRIAL REGISTRATION: ClinicalTrial.gov NCT06832852; https://clinicaltrials.gov/ct2/show/NCT06832852. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/77574.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.048 |
| Meta-epidemiology (narrow) | 0.009 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.067 | 0.008 |
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".