Nurses’ experiences with virtual care during the COVID-19 pandemic: a qualitative study in primary care
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, virtual care was used to deliver primary care services. Nurses contributed to primary care teams' capacity to deliver care virtually. This study explored nurses' roles in virtual care delivery in primary care and the barriers and facilitators that influenced their contributions. METHODS: We employed a qualitative descriptive approach and conducted semi-structured interviews with nurses representing each regulatory designation (i.e., Nurse Practitioners, Registered Nurses, Licensed/Registered Practical Nurses) working in primary care in four Canadian provinces (i.e., British Columbia, Ontario, Nova Scotia, and Newfoundland and Labrador). We performed thematic analysis on data related to the delivery of virtual care. RESULTS: We interviewed seventy-six nurses and identified three key themes and various sub-themes related to virtual nursing practice during the COVID-19 pandemic: (1) variable adoption of virtual care among nurses, (2) facilitators and barriers to virtual nursing practice, and (3) impacts of virtual delivery on care provision by nurses. Nurses' involvement in virtual care varied across designations and nurses recalled various facilitators and impediments that influenced their virtual care experience, such as guidance documents, funding models, and the availability of equipment. Virtual care influenced nurses' workflow, their ability to deliver patient-centred care, and their ability to bridge the care gap. CONCLUSIONS: Primary care teams are increasingly relying upon nurses to support virtual care delivery, emphasizing the need to understand nursing roles in virtual care. Primary care funding models should be leveraged to support nurses in virtual care delivery; and standardized learning opportunities and guidance documents focused on virtual care should be available to support primary care nurses and strengthen their contributions in future primary care teams that involve virtual nursing care.
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 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".