Predicting Registered Nurses' Behavioural Intention to Use Electronic Documentation System in Home Care: Application of an Adapted Unified Theory of Acceptance and Use of Technology Model
Bibliographic record
Abstract
The use of electronic documentation systems (EDS) has the potential to ensure timely, up-to-date and comprehensive patient health-related information is available and accessible to nurses regardless of their physical location. Despite the benefits of EDS, nurses' low intention to use such systems is well documented, which may predict behavioural usage. Further, limited knowledge exists about nurses' intention to use EDS in the context of home care. The aim of the study was to examine factors that influence nurses' intention of using EDS in home care practice. The conceptual model framing this study is adapted from the Unified Theory of Acceptance and Use of Technology (UTAUT). A cross-sectional design was used. Nurses (N = 217) currently practicing within the home care sector in Ontario participated in the study. An online survey using adapted and psychometrically sound quantitative instruments was administered. Data were analyzed with descriptive statistics and hierarchical linear regression. Performance expectancy, attitude, social influence and facilitating conditions had significant, positive and direct effects on nurses' behavioural intention. Effort expectancy and nurses' individual characteristics (i.e., age, level of education and technology experience) were not found to have a direct and/or moderating influence on nurses' intention to use EDS in home care practice. Theory, practice and research implications for the findings are presented and discussed.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".