Exploring virtual care clinical experience from non-physician healthcare providers (VCAPE)
Bibliographic record
Abstract
COVID-19 has caused an urgent implementation of virtual care (VC). Most research has focused on patient and physician experience with virtual care. Non-physician healthcare providers have played an active role in transitioning to virtual care, yet little is known about their experiences. This study explored their lived experiences in caring for patients virtually. Forty non-physician healthcare providers from local hospitals, community, and home care settings in Kingston, ON, Canada, participated and included nurse practitioners, occupational therapists, physiotherapists, psychologists, registered dietitians, social workers, and speech-language pathologists. Data were collected using semi-structured interviews between February and July 2021 and were analyzed thematically. The study was guided by organizational change theory. Four themes were identified from the data: 1) Quality of care, 2) Resources and training, 3) Healthcare system efficiency, and 4) Health equity and access for patients. Providers suggested that VC increased patient-centredness and had clear benefits for patients. Participants had little to no training in conducting patient care, virtually stating this as a key challenge. They believed that VC increased the efficiency of the healthcare system and was more proactive. Despite concerns regarding inequities across healthcare, participants reported that VC could improve equity as long as patients had access to technology. The study highlights the urgent need to support all healthcare providers in delivering optimal patient-centred care. We should leverage some of the advantages offered by VC to improve the efficiency of healthcare delivery, reduce provider burnout, and increase capacity across organizational systems.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".