Hand orthoses–related factors affecting patient satisfaction and adherence: A scoping review and checklist design
Bibliographic record
Abstract
Orthoses are essential in managing musculoskeletal conditions, but factors influencing patient satisfaction and adherence are less known. This review seeks to address this gap. Online databases (CINAHL, Embase, Scopus, PubMed, Cochrane Reviews, Web of Science, and Google Scholar) were searched without time limitation till 2024. Studies were included if they addressed hand orthoses satisfaction and adherence. Data were extracted on factors affecting satisfaction and adherence related to hand orthoses. A thematic analysis approach was employed to develop and refine a comprehensive informal checklist through expert panel consultation. After eligibility screening, we included 20 papers published between 2000 and 2023. The review identified vital orthoses-related factors for custom-made orthoses, such as comfort, durability, and fit. For 3D-printed orthoses, factors like precision, printing efficiency, and safety were highlighted, affecting adherence to orthoses use. In addition, expert consultations contributed significantly, adding factors such as initial strength and surface smoothness for custom-made orthoses, ease of readjusting, design freedom, and environmental impact for 3D-printed orthoses. Two final checklists for patients and therapists were developed based on all extracted factors, which furthered our understanding of factors influencing orthoses adherence. This review highlights the significant orthoses-related factors impacting patient satisfaction and adherence. The derived checklists are beneficial tools for therapists and patients to ensure orthoses adherence, aiming for improved therapeutic results.
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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 | 0.079 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.036 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".