How is range of motion of the fingers measured in hand therapy practice? A survey study
Bibliographic record
Abstract
Introduction A variety of techniques for measuring finger range of motion (ROM) are available for hand therapist use, however, there is no clear description of which finger ROM methods are preferred in practice. This study explored the preferred measurement techniques, the factors influencing clinical decision-making, and the clinical reasoning processes employed when faced with practice-based measurement scenarios. Methods This was a cross-sectional online survey study of hand therapists and American or Canadian Society of Hand Therapists members. Quantitative methods were employed for participant demographics and categorical clinical questions about practice patterns. Qualitative descriptive questions and vignettes were analysed using inductive and deductive content analysis, respectively. Results Four hundred and eighty-one responses were included, representing hand therapists with a median age of 51 years and median experience of 19 years. Participants preferred measuring individual joints with a goniometer ( N = 210, 44%) for perceived utility in informing treatment decisions, reliability, and confidence in measurement skills. Participants also preferred active functional ROM ( N = 117, 24%) for being quick, easy, and useful in informing treatment decisions. Participants reported using different methods with time constraints in a busy clinic, taking precautions with pins/wounds, bulky dressings/casts, pain tolerance levels of patients, or with specific pathologies. Participants’ responses to the multi-stage vignette identified distinct patterns of clinical reasoning approaches within individual vignettes. Conclusions Hand therapists use multiple methods to measure finger ROM while preferring to use goniometers to measure individual finger joints. They engage procedural and pragmatic reasoning modified by contextual factors when measuring finger ROM.
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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".