Effects of 2 Interventions on the Acceptability and Usability of a Sensing Glove for Measuring Force-Time Characteristics of Chiropractic Spinal Manipulative Therapy: A Crossover Study
Bibliographic record
Abstract
Objectives: The objective of this study was to assess chiropractors' and chiropractic students' acceptability of a sensing glove system for measuring Spinal manipulative therapy (SMT) force-time characteristics and to compare the effectiveness of 2 interventions in improving their acceptability and perceived usability. Methods: Sixteen participants were recruited and initially rated their agreement with 12 statements about the acceptability of the sensing glove system. They then underwent 2 interventions designed to improve their acceptability of the system in a random order: a 7-minute informational video and a 20-minute supervised practice session. After each intervention, participants reassessed their agreement with the acceptability statements and provided feedback on the system's usability through 10 additional statements. Data analysis used McNemar and Cochran's Q tests to assess the interventions' effectiveness. Results: The practice session led to a significantly higher number of participants agreeing with 4 more acceptability statements than at baseline, and with 1 more statement than after the video intervention. Conversely, the video intervention showed an increased proportion of agreement for only 1 statement compared to the baseline. Furthermore, the practice session resulted in a higher proportion of participants agreeing with 2 usability statements compared to the video intervention. Conclusions: The study highlights the importance of supervised hands-on practice in enhancing the acceptability and willingness of chiropractors and chiropractic students to adopt a force-sensing system in their clinical practice.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".