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Record W4200382970 · doi:10.2196/preprints.33586

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 (Preprint)

2021· preprint· en· W4200382970 on OpenAlexaffabout
Waldo Beauséjour, Simon Hagens

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicHealthcare Systems and Public Health
Canadian institutionsCanada Health Infoway
Fundersnot available
KeywordsNursingHealth careNursing careMedicinePsychologyPolitical science

Abstract

fetched live from OpenAlex

<sec> <title>BACKGROUND</title> 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. </sec> <sec> <title>OBJECTIVE</title> 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. </sec> <sec> <title>METHODS</title> 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. </sec> <sec> <title>RESULTS</title> 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 (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001), perceived quality of care at the health facility where the nurses practiced (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001), and the type of patient record–keeping system they had access to (&lt;i&gt;P&lt;/i&gt;=.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 (&lt;i&gt;χ&lt;sup&gt;2&lt;/sup&gt;&lt;/i&gt;&lt;sub&gt;4&lt;/sub&gt;=308.7; &lt;i&gt;P&lt;/i&gt;&amp;lt;.001) and knowledge (&lt;i&gt;χ&lt;sup&gt;2&lt;/sup&gt;&lt;/i&gt;&lt;sub&gt;4&lt;/sub&gt;=283.4; &lt;i&gt;P&lt;/i&gt;&amp;lt;.001) to use these technologies. </sec> <sec> <title>CONCLUSIONS</title> 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. </sec>

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.072
GPT teacher head0.360
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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