Examining Nurse Practitioner Experiences in Delivering Virtual Care During the COVID-19 Pandemic: A Mixed-Method Study
Bibliographic record
Abstract
BackgroundThe COVID-19 pandemic required Nurse Practitioners (NPs) in Fraser Health Authority (FHA) to rapidly pivot to virtual care health services. Most NPs had little to no education on providing virtual care and there is a paucity literature on how to best deliver this type of care.PurposeThe purpose of this study was to explore the experiences of NPs in FHA who were required to rapidly integrate virtual care into their practice due to the COVID-19 pandemic, while considering competencies and supports needed to integrate virtual care successfully into NP practice.MethodsThis mixed-methods study purposively sampled 41 NPs in FHA in urban British Columbia. Methods consisted of electronic surveys, and semi-structured interviews and focus groups. Data was analyzed using an interpretive description approach.ResultsThe study found a significant increase in the use of virtual care during the pandemic, with NPs reporting improved efficiency (68.3%) and comfort in virtual care delivery. However, challenges were identified in relational practice, confidence, and workflow, particularly in maintaining therapeutic relationships and conducting physical assessments. Themes from qualitative data highlighted the need for targeted education, standardized protocols, and improved technological infrastructure to support virtual care integration.ConclusionThe findings underscore the complexity of adapting to virtual care and emphasize the importance of training, policy development, and system-level supports to enhance its implementation. These results provide critical insights into the competencies required for virtual care and inform future strategies to improve its integration into NP practice in Canada and beyond.
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 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 | 0.026 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".