Factors of Adoption of Mobile Information Technology by Homecare Nurses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pervasive healthcare support through mobile information technology solutions is playing an increasing role in the attempt to improve healthcare and reduce costs. Despite the apparent attractiveness, many mobile applications have failed or have not been implemented as predicted. Among factors possibly leading to such outcomes, technology adoption is a key problem. This must be investigated early in the development process because healthcare is a particularly sensitive area with vital social implications. Moreover, it is important to investigate technology acceptance using the support of scientific tools validated for relevant information systems research. This article presents an empirical study based on the Technology Acceptance Model 2 in mobile homecare nursing. The study elicited the perceptions of 91 Canadian nurses who used personal digital assistants for 1 month in their daily activities. A partial least squares modeling data analysis revealed that nurse's perception of usefulness is the main factor in the adoption of mobile technology, having subjective norm and image within the organization as significant antecedents. Overall, this study was the first attempt at investigating scientifically, through a pertinent information systems research model, user adoption of mobile systems by homecare nursing personnel.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it