Preparing Nursing for the Virtual Care Realities of a Post-Pandemic Future
Bibliographic record
Abstract
The COVID-19 pandemic has accelerated the adoption of virtual care in healthcare contexts globally, inclusive of nursing care. Along with the rapid adoption of virtual care by the nursing profession, reflections regarding how best to leverage virtual care in nurse-patient relationships and as an innovative model of care have begun to emerge in both the literature and nursing discourse. Subsequently, the purposes of this paper are to (1) provide a reflection on the significant innovation in virtual care in nursing that was established during the early phases of the COVID-19 pandemic and (2) suggest future strategic directions for the profession in order to retain the benefits related to virtual care becoming more widely adopted into various nursing roles and activities. It is hoped that through this reflection and presentation of strategic directions, nurses and their associated leadership can identify viable future pathways to help evolve the use and delivery of virtual care in the profession for a post-pandemic world.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".