Rural patients’ experiences with anesthesia and surgical consultations in British Columbia: A survey-based comparison between virtual and in-person modalities
Bibliographic record
Abstract
Introduction: Rural patients face barriers to accessing surgical care and often need to travel long distance for pre- or post-surgical consultations. Although adaptation to the COVID-19 pandemic has demonstrated the efficacy of virtual care, there is minimal data available to evaluate patient satisfaction with this modality and consequent health service utilization if virtual services are not available. Methods: An online survey was conducted with participants living in rural British Columbia, Canada who had undergone surgery within 12 months of data collection and had either virtual or face-to-face pre- or post-surgical consultations. It was supplemented by an in-person survey administered in two rural sites to all patients who had a virtual visit prior to undergoing procedural care. A ten-point scale was used to assess satisfaction. Quantitative and qualitative data were collected and analyzed. Results: = 0.26). However, most participants indicated that virtual appointments saved them time traveling, energy, and money and made them less dependent on others, accruing significant social benefit.In the community-focused sample (n = 71), 38% said they would not have had the procedure without a virtual visit option and 21% said that they would have delayed the procedure. Virtual consultations saved patients an average of 9 h (range 1-90). Participants traveled an average of 427 kilometers round trip to have the procedures. Conclusion: Findings reveal costs and time saved in accessing care due to the introduction of pre- and post-operative virtual care visits, and further investments in virtual care are warranted. This will contribute to promoting equitable access to healthcare for rural residents.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".