Do self-management supportive interventions reduce healthcare utilization for people with musculoskeletal pain conditions? – A systematic review
Bibliographic record
Abstract
OBJECTIVE: The aim of this systematic review was to investigate the effect of self-management supportive interventions on healthcare utilization in adult cares seekers with musculoskeletal pain conditions. STUDY DESIGN: Systematic review. METHODS: We included studies comparing the effect of a self-management supportive intervention against a control intervention and included measures of healthcare utilization. Studies were searched in MEDLINE, Embase, PsycINFO, CINAHL, Pedro, and the Cochrane Library. Results were extracted for the follow-up point closest to 12 months. Risk of bias was assessed using the Cochrane Risk of Bias tool 2, and quality of evidence by The Grading of Recommendations Assessment, Development and Evaluation. Results were synthesized on study level as mean differences or odds ratios with 95 % CI. RESULTS: We included 28 studies. Eighteen studies reported on the use of primary care at follow-up, and ten, four, and 13 on specialty care, diagnostics imaging, and oral pain medication, respectively. Overall, there was very low-quality evidence for no effect of self-management interventions on healthcare utilization in all groups. All studies were classified as either having a "high risk of bias" or "some concerns". CONCLUSION: Due to substantial heterogeneity in the types and measurement of healthcare utilization outcomes, it was not feasible to conduct a meta-analysis to estimate an overall effect size. A standardized way of reporting and measuring these outcomes could aid future research in this area. The current evidence suggests that self-management supportive interventions do not affect healthcare utilization in people with musculoskeletal pain conditions, but future high-quality studies may substantially change this conclusion.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".