Care at a distance: Understanding lived experiences of people with MSK disorders receiving non-pharmacological interventions delivered through synchronous telehealth: A systematic rapid review
Bibliographic record
Abstract
Background: Little is known about lived experience of synchronous telehealth in patients with musculoskeletal (MSK) disorders. Objective: We conducted a rapid systematic review to answer: (1) what are the lived experiences and/or perspectives of people with MSK disorders receiving non-pharmacological interventions delivered through synchronous telehealth; and (2) what clinical implications can be inferred from qualitative studies focusing on lived experiences for how telehealth is delivered in the management of MSK disorders? Data sources: A comprehensive search of MEDLINE, CINAHL, PsycINFO, ProQuest, and Google Scholar from June 2010 to July 2023. Eligible qualitative and mixed methods studies capturing lived experiences of adults with MSK disorders receiving non-pharmacological interventions via synchronous telehealth were included. Study methods: Systematic rapid review conducted according to WHO guidelines. Titles and abstracts screened by reviewers independently, eligible studies critically appraised, and data was extracted. Themes summarized using the Consolidated Framework for Implementation Research (CFIR). GRADE-CERQual (Confidence in the Evidence from Reviews of Qualitative research) used to assess confidence in synthesis findings. Results: We identified 9782 references, screened 8029, and critically appraised 22, and included 17 studies. There is evidence to suggest that the experience of telehealth prior to and during the pandemic was shaped by (1) patient perception of telehealth, (2) existing relationships with practitioners, (3) availability and accessibility of telehealth technologies, and (4) perceptions about the importance of the role of the physical exam in assessing and treating MSK disorders. Conclusion: The five identified implications could be used to inform future research, policy, and strategy development.
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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 | 0.032 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".