Patient and clinician experiences of post-discharge virtual care following non-elective surgery: A qualitative descriptive study
Bibliographic record
Abstract
Remote patient monitoring (RPM) and virtual care is a burgeoning field, with considerable uptake within chronic medical populations and recently expanding to include surgical populations. Patient and clinician experiences of participating in at-home, postoperative RPM or virtual care interventions are not well described in the current literature. The objective of this study was to understand the experience of patients and clinicians who participated in a 30-day RPM and virtual care program following non-elective surgery at the beginning of the COVID-19 pandemic in Canada. The study also aimed to identify ways to improve postoperative RPM programs for future interventions. Qualitative descriptive methodology was used to describe the participants experiences of the phenomena in everyday language. Purposive, maximum variation sampling was used to recruit a heterogenous sample that included participants from all five sites of the trial and focused on recruiting patients with prior surgery. Forty-three participants (21 patients, two patient spouses, 10 nurses, 10 physicians) took part in semi-structured telephone interviews, approximately 30 minutes in length. Data was transcribed by an independent transcriptionist and confirmed by the researcher. Reflexive thematic analysis was employed, and analysis was completed by the primary researcher and triangulated with two additional researchers, guided by the Conceptual Model for Telehealth Nursing. Themes generated include: ‘Virtual care is valued and holistic care, at home’, ‘From a wide scope towards a refined intervention’, and ‘A shared responsibility: Significance of interdisciplinary collaboration’. Postoperative RPM and virtual care provides an opportunity for patients to recover at home with skilled clinical oversight, with patients and clinicians highlighting reassurance and access to clinicians during a vulnerable period, particularly within the context of the COVID-19 pandemic. Improvement for the future may include more patient-centred scheduling for interventions including reduced video calls and vital signs measurements.
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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".