Factors of Adoption of Mobile Information Technology by Homecare Nurses
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.
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 | 0.002 | 0.020 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".