Group and individual telehealth for chronic musculoskeletal pain: A scoping review
Bibliographic record
Abstract
BACKGROUND CONTEXT: Musculoskeletal (MSK) pain presents a global challenge. Individual and group pain management programmes (PMPs) are recommended approaches for patients with chronic MSK disorders. With advances in remote healthcare capability, telehealth, and the recent COVID-19 pandemic, the importance of telehealth PMPs has become even more evident. Nevertheless, it is not known how patients perceive PMPs for their MSK complaint when delivered via telehealth. OBJECTIVE: To synthesise the evidence of patients' experiences of group and individual telehealth PMPs for chronic MSK pain. DESIGN: A scoping review informed by the PRISMA extension for scoping reviews. DATA SOURCES: Based on a planned search strategy, modified following initial searches, an electronic search was conducted of key databases: Cochrane Library, Medline, CINAHL, EMBASE, AMED, SportDiscus and APA PsychInfo from 2010 until 11 May 2021. STUDY SELECTION: Any qualitative or mixed methods study reporting patient experiences of telehealth PMPs for patients with MSK disorders. DATA EXTRACTION AND DATA SYNTHESIS: Data were extracted and synthesised using thematic analysis. RESULTS: From 446 identified studies, 10 were included. Just two studies investigated group telehealth PMPs for patients with MSK disorders, with eight delivered individually. Four main themes emerged: (1) Usability of the technology, (2) Tailored care, (3) Therapeutic alliance and (4) Managing behaviour. The findings highlight patient acceptability of telehealth to support self-management for chronic MSK disorders, with appropriate clinical and technical support. Group telehealth has the potential to empower patients with peer support. Remote delivery of PMPs also impacts on how patients and providers interact, communicate and develop a therapeutic relationship. CONCLUSIONS AND IMPLICATIONS: Barriers and enablers to engagement in telehealth PMPs for patients with chronic MSK disorders have been identified. Peer support and group cohesiveness can be achieved remotely to enhance the patient experience. There is a critical need for further research in this area.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".