Effectiveness of motivational interviewing on pain-related outcomes in patients with musculoskeletal pain: a systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: To determine the effectiveness of motivational interviewing (MI) in the treatment of adult patients with musculoskeletal (MSK) pain. MATERIALS AND METHODS: A comprehensive search strategy without date or language restrictions was performed in five scientific databases. Manual searches and reference tracking were also carried out. Two reviewers independently performed title and abstract as well as full-text screening, data extraction, assessment of risk of bias, and evaluation of overall certainty with the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. Where possible, a meta-analysis was performed to identify effects across multiple studies. RESULTS: Ten studies reported in 16 manuscripts met the previously defined eligibility criteria. Many outcomes could be extracted and analyzed. Nine of the ten studies were considered to have a high risk of bias according to the revised Cochrane RoB 2.0. The majority of comparisons were based on low to very low overall certainty of the evidence. CONCLUSIONS: The existing literature did not allow a reliable statement about the effectiveness of MI on pain-related outcomes in people with MSK pain. Only tendencies towards a positive influence of MI were recognisable. However, this must be confirmed by high-quality randomized controlled trials in the future. IMPLICATIONS FOR REHABILITATIONMotivational interviewing appears to be an option for patients with chronic musculoskeletal pain which presumably has few to no adverse effects.Motivational interviewing could represent an opportunity, particularly for patients for whom other treatment strategies have been ineffective so far.Motivational interviewing can safely be combined with other forms of treatment such as an active exercise therapy.
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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".