Determining the technology acceptance and usability of a new device for hand therapy: A qualitative description study (Preprint)
Bibliographic record
Abstract
BACKGROUND Upper extremity function is critical for daily living across domains; this may include completing activities of daily living, employment, and participating in recreational activities. The FEPSim device is a medical device for hand and wrist rehabilitation which can be adjusted according to the patient’s requirements in rehabilitation. Furthermore, the FEPSim can be used to assess the patient’s strength and range of motion of the forearm, wrist, and hand. At present, the acceptance and usability of the FEPSim have not been tested in a clinical setting, with limited perspectives from rehabilitation-providing clinicians OBJECTIVE To understand the factors related to the acceptance and usability of the FEPSim device. Upper limb disorders are prevalent across populations. The impact of upper limb disorders, both acute and chronic, puts a significant burden on the Canadian healthcare system METHODS A qualitative descriptive study was conducted that involved face-to-face semi-structured interviews with hand therapists from hand therapy services who used the FEPSim device. We used purposive sampling to recruit ten participants over a period of fourteen months. Semi-structured interview questions (topic guided) examined the technology acceptance and usability of the FEPSim device. RESULTS Six factors were found to be critical aspects of the acceptance and usability of the FEPSim device. These factors were useful for therapy, effortlessness, environmental conditions, internal encouragement, technological aesthetics, and use. CONCLUSIONS The FEPSim device was widely accepted by therapists. The use of the FEPSim device is a feasible alternative for supporting hand therapy. CLINICALTRIAL Not applicable
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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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".