Virtual surgical consultation during the COVID-19 pandemic: a patient-oriented, cross-sectional study using telephone interviews
Bibliographic record
Abstract
Background: Health care delivery shifted rapidly during the COVID-19 pandemic, whereby virtual consultations replaced many face-to-face interactions. We sought to gather patient perspectives on their experiences with virtual surgical consultation, the advantages and disadvantages of this delivery method and their overall satisfaction with virtual appointments. Methods: We conducted a patient-oriented, cross-sectional study. Adult patients (age > 18 yr) who had a virtual consultation with a participating general surgeon in Saskatoon, Saskatchewan, from April to May 2020 were eligible. We conducted telephone interviews using open- and close-ended questions. We used thematic analysis to determine themes from the qualitative data. As research team members, 2 patient partners were involved in identifying priorities, developing the research question, designing research methods, analyzing data and disseminating findings. We analyzed and presented quantitative data descriptively. Results: We interviewed 45 participants from 7 general surgery practices; the average age was 62 years. Most participants lived outside Saskatoon and had virtual follow-up appointments. The 3 themes related to advantages of virtual consultations were convenience, cost savings and decreased exposure to pathogens. The 4 themes related to their disadvantages were that they were not as personal, the surgeon was not able to perform a physical examination, and there were issues with scheduling and issues with technology. Most participants were satisfied with the care they received (n = 41) and would be willing to use virtual consultation in the future (n = 31). Interpretation: We found that virtual consultations are an effective and efficient way to deliver surgical care but are not appropriate for every situation and cannot completely replace face-to-face interactions. Our study identified the advantages and disadvantages of virtual surgical consultation to help better guide the delivery of virtual care in the future.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".