Systematic Review of Costs and Effects of Self-Management Interventions for Chronic Musculoskeletal Pain: Spotlight on Analytic Perspective and Outcomes Assessment
Bibliographic record
Abstract
Background: Evidence for the cost-effectiveness of self-management interventions for chronic musculoskeletal pain (CMP) lacks consensus, which may be due to variability in the costing methods employed. Purpose: The purposes of the study were to identify how costs and effects have been assessed in economic analysis of self-management interventions for CMP and to identify the effect of the chosen analytical perspective on cost-effectiveness conclusions. Data Sources: Five databases were searched for all study designs using relevant terms. Study Selection: Two independent researchers reviewed all titles for predefined inclusion criteria: adults (≥18 years of age) with CMP, interventions with a primary aim of promoting self-management, and conducted a cost analysis. Data Extraction: Descriptive data including population, self-management intervention, analytical perspective, and costs and effects measured were collected by one reviewer and checked for accuracy by a second reviewer. Data Synthesis: Fifty-seven studies were identified: 65% (n = 37) chose the societal perspective, of which 89% (n = 33) captured health care utilization, 92% (n = 34) reported labor productivity, 65% (n = 24) included intervention delivery, and 59% (n = 22) captured patient/family costs. Types of costs varied in all studies. Eight studies conducted analyses from both health service and societal perspectives; cost-effectiveness estimates varied with perspective chosen, but in no case was the difference sufficient to change overall policy recommendations. Limitations: Chronic musculoskeletal pain conditions where self-management is recommended, but not as a primary treatment, were excluded. Gray literature was excluded. Conclusion: Substantial heterogeneity in the cost components captured in the assessment of self-management for CMP was found; this was independent of the analytic perspective used. Greater efforts to ensure complete and consistent costings are required if reliable cost-effectiveness evidence of self-management interventions is to be generated and to inform the most appropriate perspective for economic analyses in this field.
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 | 0.090 | 0.365 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.014 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".