What factors determine therapists' acceptance of new technologies for rehabilitation – a study using the Unified Theory of Acceptance and Use of Technology (UTAUT)
Bibliographic record
Abstract
Purpose: The aim of this study was to examine what factors affect the acceptance behavior and use of new technologies for rehabilitation by therapists at a large rehabilitation hospital in Canada. Method: A self-administrated paper-based survey was created by adapting scales with high levels of internal consistency in prior research using the Unified Theory of Acceptance and Use of Technology (UTAUT). Items were scored on a 7-point Likert scale, ranging from "strongly disagree (1)" to "strongly agree (7)". The target population was all occupational therapists (OT) and physical therapists (PT) involved with the provision of therapeutic interventions at the hospital. Our research model was tested using partial least squares (PLS) technique. Results: Performance expectancy was the strongest salient construct for behavioral intention to use new technologies in rehabilitation, whereas neither effort expectancy nor social influence were salient constructs for behavioral intention to use new technologies; (4) facilitating condition and behavioral intention to use new technologies were salient constructs for current use of new technologies in rehabilitation, with facilitating condition the strongest salient for current use of new technologies in rehabilitation. Conclusion: In a large rehabilitation hospital where use of new technologies in rehabilitation is not mandatory, performance expectancy, or how the technology can help in therapists' work, was the most important factor in determining therapists' acceptance and use of technologies. However, effort expectancy and social influence constructs were not important, i.e. therapists were not influenced by the degree of difficulty or social pressures to use technologies. Behavioral intention and facilitating condition, or institutional support, are related to current use of new technologies in rehabilitation.Implications for RehabilitationRehabilitation professionals who are faced with using new technologies are less concerned about effort and social pressures, than they are about what the technologies can do for them or their clients.When it comes to new rehabilitation technologies, actual users express intention.Rehabilitation professionals' acceptance and adoption of technologies rely on conditions that facilitate their use. These conditions include scheduling, support and a conductive environment.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".