Uncovering Important Drivers of the Increase in the Use of Virtual Care Technologies in Nursing Care: Quantitative Analysis From the 2020 National Survey of Canadian Nurses
Bibliographic record
Abstract
Background Canadian nurses are at the forefront of patient care delivery. Although the use of digital health technologies for care delivery is gaining momentum in Canada, nurses are encouraged to integrate virtual care into their practice. In early 2020, more Canadian nurses delivered care virtually compared with 3 years ago. Objective This study seeks to uncover the professional characteristics of Canadian nurses accessing virtual care in 2020, understand how these characteristics differ across types of technologies, investigate whether the nurses accessing virtual care possess the skills and knowledge needed to use these technologies, and determine the important drivers of the uptake of virtual care observed in 2020. Methods We used data from the 2017 and 2020 National Survey of Canadian Nurses. This survey collected data on the use of digital health technologies in nursing practice. It concerned regulated nursing professionals working in different health care settings and from different domains of nursing practice. We combined the chi-square independence test and logistic regression analysis to uncover the most relevant drivers of virtual care uptake by nurses in 2020. Results In early 2020, before the declaration of the COVID-19 pandemic, nurses who delivered care virtually were predominantly nurse practitioners (135/159, 84.9%) and more likely to work in a primary or community care setting (202/367, 55%) and in an urban setting (194/313, 61.9%). Factors such as nursing designation (P<.001), perceived quality of care at the health facility where the nurses practiced (P<.001), and the type of patient record–keeping system they had access to (P=.04) had a statistically significant effect on the probability of nurses to deliver care virtually in early 2020. Furthermore, nurses’ perception of the quality of care they delivered through virtual technologies was statistically associated with their perception of the skills (χ24=308.7; P<.001) and knowledge (χ24=283.4; P<.001) to use these technologies. Conclusions This study emphasizes the critical importance of nursing designation, geographic location, and type of patient record–keeping system in predicting virtual care integration in nursing practice. The findings related to geographic location can be used by decision-makers for better allocation of digital health resources among care settings in rural and urban areas. Similarly, the disparities observed across nursing designations have some implications for the digital training of nurses at all levels of practice. Finally, the association between electronic medical record use and uptake of virtual care could accelerate the implementation of more modernized record-keeping systems in care settings. Hence, this could advance interoperability and improve health care delivery.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".