Rural patients’ experiences with anesthesia and surgical consultations in British Columbia: A survey-based comparison between virtual and in-person modalities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it